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<div class="title">TensorBlock.h</div>  </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">// This file is part of Eigen, a lightweight C++ template library</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment">// for linear algebra.</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment">// This Source Code Form is subject to the terms of the Mozilla</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment">// Public License v. 2.0. If a copy of the MPL was not distributed</span></div>
<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment">// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.</span></div>
<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160; </div>
<div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="preprocessor">#ifndef EIGEN_CXX11_TENSOR_TENSOR_BLOCK_H</span></div>
<div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="preprocessor">#define EIGEN_CXX11_TENSOR_TENSOR_BLOCK_H</span></div>
<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160; </div>
<div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="preprocessor">#include &quot;./InternalHeaderCheck.h&quot;</span></div>
<div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160; </div>
<div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespaceEigen.html">Eigen</a> {</div>
<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="keyword">namespace </span>internal {</div>
<div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160; </div>
<div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="comment">// -------------------------------------------------------------------------- //</span></div>
<div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="comment">// Forward declarations for templates defined below.</span></div>
<div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Scalar, <span class="keyword">typename</span> IndexType, <span class="keywordtype">int</span> NumDims, <span class="keywordtype">int</span> Layout&gt;</div>
<div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="keyword">class </span>TensorBlockIO;</div>
<div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160; </div>
<div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;<span class="comment">// -------------------------------------------------------------------------- //</span></div>
<div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="comment">// Helper function to compute strides for densely stored buffer of given</span></div>
<div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="comment">// dimensions.</span></div>
<div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160; </div>
<div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;<span class="comment">// TODO(ezhulenev): We compute strides 1000 times in different evaluators, use</span></div>
<div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;<span class="comment">// this function instead everywhere.</span></div>
<div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;<span class="keyword">template</span> &lt;<span class="keywordtype">int</span> Layout, <span class="keyword">typename</span> IndexType, <span class="keywordtype">int</span> NumDims&gt;</div>
<div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;EIGEN_ALWAYS_INLINE DSizes&lt;IndexType, NumDims&gt; strides(</div>
<div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;    <span class="keyword">const</span> DSizes&lt;IndexType, NumDims&gt;&amp; dimensions) {</div>
<div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;  DSizes&lt;IndexType, NumDims&gt; strides;</div>
<div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;  <span class="keywordflow">if</span> (NumDims == 0) <span class="keywordflow">return</span> strides;</div>
<div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160; </div>
<div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;  <span class="comment">// TODO(ezhulenev): Use templates to unroll this loop (similar to</span></div>
<div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;  <span class="comment">// h_array_reduce in CXX11meta.h)? Benchmark it.</span></div>
<div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;  <span class="keywordflow">if</span> (<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(Layout) == <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(<a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a0103672ae41005ab03b4176c765afd62">ColMajor</a>)) {</div>
<div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;    strides[0] = 1;</div>
<div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 1; i &lt; NumDims; ++i) {</div>
<div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;      strides[i] = strides[i - 1] * dimensions[i - 1];</div>
<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;    }</div>
<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;  } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;    strides[NumDims - 1] = 1;</div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = NumDims - 2; i &gt;= 0; --i) {</div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;      strides[i] = strides[i + 1] * dimensions[i + 1];</div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;    }</div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;  }</div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160; </div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;  <span class="keywordflow">return</span> strides;</div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;}</div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160; </div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;<span class="keyword">template</span> &lt;<span class="keywordtype">int</span> Layout, <span class="keyword">typename</span> IndexType, <span class="keywordtype">size_t</span> NumDims&gt;</div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;EIGEN_ALWAYS_INLINE DSizes&lt;IndexType, NumDims&gt; strides(</div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;    <span class="keyword">const</span> Eigen::array&lt;IndexType, NumDims&gt;&amp; dimensions) {</div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;  <span class="keywordflow">return</span> strides&lt;Layout&gt;(DSizes&lt;IndexType, NumDims&gt;(dimensions));</div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;}</div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160; </div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;<span class="keyword">template</span> &lt;<span class="keywordtype">int</span> Layout, std::ptrdiff_t... Indices&gt;</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;EIGEN_STRONG_INLINE DSizes&lt;std::ptrdiff_t, <span class="keyword">sizeof</span>...(Indices)&gt; strides(</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;    <span class="keyword">const</span> Sizes&lt;Indices...&gt;&amp; sizes) {</div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;  <span class="keywordflow">return</span> strides&lt;Layout&gt;(DSizes&lt;std::ptrdiff_t, <span class="keyword">sizeof</span>...(Indices)&gt;(sizes));</div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;}</div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160; </div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;<span class="comment">// -------------------------------------------------------------------------- //</span></div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160; </div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;<span class="comment">// Tensor block shape type defines what are the shape preference for the blocks</span></div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;<span class="comment">// extracted from the larger tensor.</span></div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;<span class="comment">// Example: blocks of 100 elements from the large 100x100 tensor:</span></div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;<span class="comment">// - tensor: 100x100</span></div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;<span class="comment">// - target_block_size: 100</span></div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;<span class="comment">// TensorBlockShapeType:</span></div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;<span class="comment">//  - kUniformAllDims: 100 blocks of size 10x10</span></div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;<span class="comment">//  - kSkewedInnerDims: 100 blocks of size 100x1 (or 1x100 depending on a column</span></div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;<span class="comment">//                      or row major layout)</span></div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;<span class="keyword">enum class</span> TensorBlockShapeType { kUniformAllDims, kSkewedInnerDims };</div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160; </div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;<span class="keyword">struct </span>TensorBlockResourceRequirements {</div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;  TensorBlockShapeType shape_type;  <span class="comment">// target block shape</span></div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;  <span class="keywordtype">size_t</span> size;                      <span class="comment">// target block size</span></div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;  TensorOpCost cost_per_coeff;      <span class="comment">// cost of computing a single block element</span></div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160; </div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;<span class="preprocessor">#ifdef EIGEN_HIPCC</span></div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;  <span class="comment">// For HIPCC, we need to explicitly declare as a &quot;device fun&quot;, the constructor</span></div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;  <span class="comment">// which is implicitly invoked in the &quot;merge&quot; / &quot;any&quot; routines. else HIPCC</span></div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;  <span class="comment">// errors out complaining about the lack of a matching constructor</span></div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;  EIGEN_DEVICE_FUNC</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;  TensorBlockResourceRequirements(TensorBlockShapeType shape_type_, <span class="keywordtype">size_t</span> size_,</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;                                  TensorOpCost cost_)</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;    : shape_type(shape_type_), size(size_), cost_per_coeff(cost_)</div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;  {}</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;<span class="preprocessor">#endif</span></div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160; </div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Scalar&gt;</div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;  EIGEN_DEVICE_FUNC <span class="keyword">static</span> TensorBlockResourceRequirements withShapeAndSize(</div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;      TensorBlockShapeType shape_type, <span class="keywordtype">size_t</span> size_in_bytes,</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;      TensorOpCost cost) {</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> size = numext::maxi(<span class="keywordtype">size_t</span>(1), size_in_bytes / <span class="keyword">sizeof</span>(Scalar));</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;    <span class="keywordflow">return</span> {shape_type, size, cost};</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;  }</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160; </div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Scalar&gt;</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;  EIGEN_DEVICE_FUNC <span class="keyword">static</span> TensorBlockResourceRequirements withShapeAndSize(</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;      TensorBlockShapeType shape_type, <span class="keywordtype">size_t</span> size_in_bytes) {</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;    <span class="comment">// This default cost per coefficient is valid for most materialized tensor</span></div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;    <span class="comment">// block evaluation implementations, because they typically just read</span></div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    <span class="comment">// coefficients from the underlying tensor storage, and write to the tensor</span></div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;    <span class="comment">// block buffer (scratch or destination memory, reads and writes have linear</span></div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;    <span class="comment">// access pattern). We ignore the fixed cost of block evaluation, because in</span></div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;    <span class="comment">// practice it should negligible.</span></div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    <span class="comment">//</span></div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;    <span class="comment">// Lazy block evaluation adds the cost of calling a functor for each</span></div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;    <span class="comment">// coefficient.</span></div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;    <span class="comment">//</span></div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;    <span class="comment">// All non-trivial block evaluation implementations must provide their own</span></div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;    <span class="comment">// cost approximation (e.g. shuffling inner dimension has a much higher cost</span></div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;    <span class="comment">// because it reads memory randomly, although the total number of moved</span></div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;    <span class="comment">// bytes is the same).</span></div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;    <span class="keywordflow">return</span> withShapeAndSize&lt;Scalar&gt;(shape_type, size_in_bytes,</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;                                    {<span class="comment">/*bytes_loaded=*/</span><span class="keyword">sizeof</span>(Scalar),</div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;                                     <span class="comment">/*bytes_stored=*/</span><span class="keyword">sizeof</span>(Scalar),</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;                                     <span class="comment">/*compute_cycles=*/</span>0});</div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;  }</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160; </div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Scalar&gt;</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;  EIGEN_DEVICE_FUNC <span class="keyword">static</span> TensorBlockResourceRequirements skewed(</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;      <span class="keywordtype">size_t</span> size_in_bytes) {</div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    <span class="keywordflow">return</span> withShapeAndSize&lt;Scalar&gt;(TensorBlockShapeType::kSkewedInnerDims,</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;                                    size_in_bytes);</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;  }</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160; </div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Scalar&gt;</div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;  EIGEN_DEVICE_FUNC <span class="keyword">static</span> TensorBlockResourceRequirements uniform(</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;      <span class="keywordtype">size_t</span> size_in_bytes) {</div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;    <span class="keywordflow">return</span> withShapeAndSize&lt;Scalar&gt;(TensorBlockShapeType::kUniformAllDims,</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;                                    size_in_bytes);</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;  }</div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160; </div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;  EIGEN_DEVICE_FUNC</div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;  <span class="keyword">static</span> EIGEN_STRONG_INLINE TensorBlockResourceRequirements</div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;  merge(<span class="keyword">const</span> TensorBlockResourceRequirements&amp; lhs,</div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;        <span class="keyword">const</span> TensorBlockResourceRequirements&amp; rhs) {</div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;    <span class="keywordflow">return</span> {merge(lhs.shape_type, rhs.shape_type),           <span class="comment">// shape_type</span></div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;            merge(lhs.size, rhs.size),                       <span class="comment">// size</span></div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;            merge(lhs.cost_per_coeff, rhs.cost_per_coeff)};  <span class="comment">// cost_per_coeff</span></div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;  }</div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160; </div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;  EIGEN_DEVICE_FUNC TensorBlockResourceRequirements&amp; addCostPerCoeff(</div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;      TensorOpCost cost) {</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;    cost_per_coeff += cost;</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;    <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;  }</div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160; </div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;  <span class="comment">// This is a resource requirement that should be returned from expressions</span></div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;  <span class="comment">// that do not have any block evaluation preference (e.g. default tensor</span></div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;  <span class="comment">// expression with raw buffer access).</span></div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;  EIGEN_DEVICE_FUNC</div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;  <span class="keyword">static</span> EIGEN_STRONG_INLINE TensorBlockResourceRequirements any() {</div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;    <span class="keywordflow">return</span> {TensorBlockShapeType::kUniformAllDims, 1, {0, 0, 0}};</div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;  }</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160; </div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160; <span class="keyword">private</span>:</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;  <span class="keyword">using</span> Requirements = TensorBlockResourceRequirements;</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160; </div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;  EIGEN_DEVICE_FUNC</div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;  <span class="keyword">static</span> EIGEN_STRONG_INLINE <span class="keywordtype">size_t</span> merge(<span class="keywordtype">size_t</span> lhs_size, <span class="keywordtype">size_t</span> rhs_size) {</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;    <span class="keywordflow">return</span> numext::maxi(lhs_size, rhs_size);</div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;  }</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160; </div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;  EIGEN_DEVICE_FUNC</div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;  <span class="keyword">static</span> EIGEN_STRONG_INLINE TensorBlockShapeType</div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;  merge(TensorBlockShapeType lhs, TensorBlockShapeType rhs) {</div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;    <span class="keywordflow">return</span> (lhs == TensorBlockShapeType::kSkewedInnerDims ||</div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;            rhs == TensorBlockShapeType::kSkewedInnerDims)</div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;               ? TensorBlockShapeType::kSkewedInnerDims</div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;               : TensorBlockShapeType::kUniformAllDims;</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;  }</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160; </div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;  EIGEN_DEVICE_FUNC</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;  <span class="keyword">static</span> EIGEN_STRONG_INLINE TensorOpCost merge(TensorOpCost lhs_cost,</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;                                                TensorOpCost rhs_cost) {</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;    <span class="keywordflow">return</span> lhs_cost + rhs_cost;</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;  }</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;};</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160; </div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;<span class="comment">// -------------------------------------------------------------------------- //</span></div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;<span class="comment">// TensorBlockDescriptor specifies a block offset within a tensor and the block</span></div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;<span class="comment">// sizes along each of the tensor dimensions.</span></div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160; </div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;<span class="keyword">template</span> &lt;<span class="keywordtype">int</span> NumDims, <span class="keyword">typename</span> IndexType = Eigen::Index&gt;</div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;<span class="keyword">class </span>TensorBlockDescriptor {</div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;  <span class="keyword">typedef</span> DSizes&lt;IndexType, NumDims&gt; Dimensions;</div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160; </div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;  <span class="comment">// If we evaluate a Tensor assignment, and expression on the left, already has</span></div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;  <span class="comment">// a memory buffer, then we might do performance optimization, and evaluate</span></div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;  <span class="comment">// the root expression directly into the final output memory. Some time it&#39;s</span></div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;  <span class="comment">// possible to reuse it for materializing subexpressions inside an expression</span></div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;  <span class="comment">// tree, to to avoid dynamic memory allocation.</span></div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;  <span class="comment">//</span></div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;  <span class="comment">// The pointer type of the underlying storage is erased, because passing</span></div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;  <span class="comment">// Scalar type through all the expression evaluation layers is way too many</span></div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;  <span class="comment">// templates. In practice destination buffer type should always match the</span></div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;  <span class="comment">// evaluated expression scalar type.</span></div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;  <span class="keyword">class </span>DestinationBuffer {</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;   <span class="keyword">public</span>:</div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;    <span class="keyword">enum</span> DestinationBufferKind : <span class="keywordtype">int</span> {</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;      <span class="comment">// The above explicit specification of &quot;int&quot; as the enum basetype is</span></div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;      <span class="comment">// needed to get around a HIPCC link error (&quot;the field type is not</span></div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;      <span class="comment">// amp-compatible&quot;)</span></div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;      <span class="comment">// which is issued for class members with the enum type.</span></div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;      <span class="comment">// TODO(rocm):</span></div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;      <span class="comment">// remove the &quot;int&quot; basetype once HIPCC has been fixed to not error out</span></div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;      <span class="comment">// in the above scenario.</span></div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160; </div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;      <span class="comment">// Destination buffer is not defined (`m_data` == nullptr).</span></div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;      kEmpty,</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160; </div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;      <span class="comment">// Tensor block defined by an owning tensor block descriptor can fit</span></div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;      <span class="comment">// contiguously into the destination buffer. In this case it&#39;s safe to</span></div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;      <span class="comment">// materialize tensor block in the destination buffer, wrap it in a</span></div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;      <span class="comment">// TensorMap, and use to build Eigen expression on top of it.</span></div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;      kContiguous,</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160; </div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;      <span class="comment">// Destination buffer strides do not match strides of the contiguously</span></div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;      <span class="comment">// stored block, and it&#39;s impossible to define a TensorMap over this</span></div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;      <span class="comment">// buffer. However if we are evaluating a root of an expression tree, we</span></div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;      <span class="comment">// still can materialize an output into this destination, because we can</span></div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;      <span class="comment">// guarantee that no one will ever access it through block API.</span></div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;      <span class="comment">//</span></div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;      <span class="comment">// In theory it is possible to build valid TensorStriding&lt;TensorMap&gt;</span></div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;      <span class="comment">// expression on top of this destination buffer, however it has</span></div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;      <span class="comment">// inefficient coeff/packet access, and defeats the purpose of fast block</span></div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;      <span class="comment">// evaluation API.</span></div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;      kStrided</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;    };</div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160; </div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;    <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Scalar&gt;</div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;    Scalar* data()<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;      eigen_assert(m_data_type_size == <span class="keyword">sizeof</span>(Scalar));</div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;      <span class="keywordflow">return</span> <span class="keyword">static_cast&lt;</span>Scalar*<span class="keyword">&gt;</span>(m_data);</div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;    }</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160; </div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;    <span class="keyword">const</span> Dimensions&amp; strides()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_strides; }</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;    <span class="keyword">const</span> DestinationBufferKind&amp; kind()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_kind; }</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160; </div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;   <span class="keyword">private</span>:</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;    <span class="keyword">friend</span> <span class="keyword">class </span>TensorBlockDescriptor&lt;NumDims, IndexType&gt;;</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160; </div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;    DestinationBuffer() : m_data(NULL), m_data_type_size(0), m_kind(kEmpty) {}</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160; </div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;    <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Scalar&gt;</div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;    DestinationBuffer(Scalar* data, <span class="keyword">const</span> Dimensions&amp; strides,</div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;                      DestinationBufferKind kind)</div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;        : m_data(static_cast&lt;void*&gt;(data)),</div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;          m_data_type_size(sizeof(Scalar)),</div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;          m_strides(strides),</div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;          m_kind(kind) {}</div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160; </div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;    <span class="keyword">template</span> &lt;<span class="keywordtype">int</span> Layout, <span class="keyword">typename</span> Scalar&gt;</div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;    <span class="keyword">static</span> DestinationBuffer make(<span class="keyword">const</span> TensorBlockDescriptor&amp; desc,</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;                                  Scalar* data, <span class="keyword">const</span> Dimensions&amp; strides) {</div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;      <span class="keywordflow">return</span> DestinationBuffer(data, strides, kind&lt;Layout&gt;(desc, strides));</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;    }</div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160; </div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;    <span class="keyword">template</span> &lt;<span class="keywordtype">int</span> Layout&gt;</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;    <span class="keyword">static</span> DestinationBufferKind kind(<span class="keyword">const</span> TensorBlockDescriptor&amp; desc,</div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;                                      <span class="keyword">const</span> Dimensions&amp; strides) {</div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;      <span class="keyword">const</span> Dimensions&amp; desc_dims = desc.dimensions();</div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;      <span class="keyword">const</span> Dimensions&amp; desc_strides = internal::strides&lt;Layout&gt;(desc_dims);</div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; NumDims; ++i) {</div>
<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;        <span class="keywordflow">if</span> (desc_dims[i] == 1) <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;        <span class="keywordflow">if</span> (desc_strides[i] != strides[i]) <span class="keywordflow">return</span> kStrided;</div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;      }</div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;      <span class="keywordflow">return</span> kContiguous;</div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;    }</div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160; </div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;    <span class="comment">// Storage pointer is type erased, to reduce template bloat, but we still</span></div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    <span class="comment">// keep the size of the underlying element type for error checking.</span></div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;    <span class="keywordtype">void</span>* m_data;</div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;    <span class="keywordtype">size_t</span> m_data_type_size;</div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160; </div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;    <span class="comment">// Destination buffer dimensions always match the dimensions of a tensor</span></div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;    <span class="comment">// block descriptor it belongs to, however strides might be different.</span></div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;    Dimensions m_strides;</div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160; </div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;    DestinationBufferKind m_kind;</div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;  };</div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160; </div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;  TensorBlockDescriptor(<span class="keyword">const</span> IndexType offset, <span class="keyword">const</span> Dimensions&amp; dimensions,</div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;                        <span class="keyword">const</span> DestinationBuffer&amp; destination)</div>
<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;      : m_offset(offset),</div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;        m_dimensions(dimensions),</div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;        m_destination(destination) {}</div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160; </div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;  TensorBlockDescriptor(<span class="keyword">const</span> IndexType offset, <span class="keyword">const</span> Dimensions&amp; dimensions)</div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;      : m_offset(offset),</div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;        m_dimensions(dimensions),</div>
<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;        m_destination(DestinationBuffer()) {}</div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160; </div>
<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;  IndexType offset()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_offset; }</div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;  <span class="keyword">const</span> Dimensions&amp; dimensions()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_dimensions; }</div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;  IndexType dimension(<span class="keywordtype">int</span> index)<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_dimensions[index]; }</div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;  IndexType size()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> array_prod&lt;IndexType&gt;(m_dimensions); }</div>
<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160; </div>
<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;  <span class="keyword">const</span> DestinationBuffer&amp; destination()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_destination; }</div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160; </div>
<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;  <span class="keyword">template</span> &lt;<span class="keywordtype">int</span> Layout, <span class="keyword">typename</span> Scalar&gt;</div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;  <span class="keywordtype">void</span> AddDestinationBuffer(Scalar* dst_base, <span class="keyword">const</span> Dimensions&amp; dst_strides) {</div>
<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;    eigen_assert(dst_base != NULL);</div>
<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;    m_destination =</div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;        DestinationBuffer::template make&lt;Layout&gt;(*<span class="keyword">this</span>, dst_base, dst_strides);</div>
<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;  }</div>
<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160; </div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;  <span class="keyword">template</span> &lt;<span class="keywordtype">int</span> Layout, <span class="keyword">typename</span> Scalar, <span class="keyword">typename</span> DstStr<span class="keywordtype">id</span>esIndexType&gt;</div>
<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;  <span class="keywordtype">void</span> AddDestinationBuffer(</div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;      Scalar* dst_base,</div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;      <span class="keyword">const</span> DSizes&lt;DstStridesIndexType, NumDims&gt;&amp; dst_strides) {</div>
<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;    <span class="comment">// DSizes constructor will do index type promotion if it&#39;s safe.</span></div>
<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;    AddDestinationBuffer&lt;Layout&gt;(dst_base, Dimensions(dst_strides));</div>
<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;  }</div>
<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160; </div>
<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;  TensorBlockDescriptor&amp; DropDestinationBuffer() {</div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;    m_destination.m_data = NULL;</div>
<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;    m_destination.m_kind = DestinationBuffer::kEmpty;</div>
<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;    <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;  }</div>
<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160; </div>
<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;  <span class="keywordtype">bool</span> HasDestinationBuffer()<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;    <span class="keywordflow">return</span> m_destination.kind() != DestinationBuffer::kEmpty;</div>
<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;  }</div>
<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160; </div>
<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;  <span class="comment">// Returns a copy of `*this` with updated offset.</span></div>
<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;  TensorBlockDescriptor WithOffset(IndexType offset)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;    <span class="keywordflow">return</span> TensorBlockDescriptor(offset, m_dimensions, m_destination);</div>
<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;  }</div>
<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160; </div>
<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160; <span class="keyword">private</span>:</div>
<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;  <span class="comment">// Offset and dimensions are immutable after construction. Block descriptor</span></div>
<div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;  <span class="comment">// can only be mutated by adding or dropping destination.</span></div>
<div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;  <span class="keyword">const</span> IndexType m_offset;</div>
<div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;  <span class="keyword">const</span> Dimensions m_dimensions;</div>
<div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;  DestinationBuffer m_destination;</div>
<div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;};</div>
<div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160; </div>
<div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;<span class="comment">// -------------------------------------------------------------------------- //</span></div>
<div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;<span class="comment">// TensorBlockMapper is responsible for iterating over the blocks of a tensor.</span></div>
<div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160; </div>
<div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;<span class="keyword">template</span> &lt;<span class="keywordtype">int</span> NumDims, <span class="keywordtype">int</span> Layout, <span class="keyword">typename</span> IndexType = Eigen::Index&gt;</div>
<div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;<span class="keyword">class </span>TensorBlockMapper {</div>
<div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;  <span class="keyword">typedef</span> TensorBlockDescriptor&lt;NumDims, IndexType&gt; BlockDescriptor;</div>
<div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160; </div>
<div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;  <span class="keyword">typedef</span> DSizes&lt;IndexType, NumDims&gt; Dimensions;</div>
<div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160; </div>
<div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;  TensorBlockMapper() = <span class="keywordflow">default</span>;</div>
<div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;  TensorBlockMapper(<span class="keyword">const</span> DSizes&lt;IndexType, NumDims&gt;&amp; dimensions,</div>
<div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;                    <span class="keyword">const</span> TensorBlockResourceRequirements&amp; requirements)</div>
<div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;      : m_tensor_dimensions(dimensions), m_requirements(requirements) {</div>
<div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;    <span class="comment">// Compute block dimensions and the total number of blocks.</span></div>
<div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;    InitializeBlockDimensions();</div>
<div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;  }</div>
<div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160; </div>
<div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE IndexType blockCount()<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;    <span class="keywordflow">return</span> m_total_block_count;</div>
<div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;  }</div>
<div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160; </div>
<div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE IndexType blockTotalSize()<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;    <span class="keywordflow">return</span> m_block_dimensions.TotalSize();</div>
<div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;  }</div>
<div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160; </div>
<div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE <span class="keyword">const</span> DSizes&lt;IndexType, NumDims&gt;&amp;</div>
<div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;  blockDimensions()<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;    <span class="keywordflow">return</span> m_block_dimensions;</div>
<div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;  }</div>
<div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160; </div>
<div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockDescriptor</div>
<div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;  blockDescriptor(IndexType block_index)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;    <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> isColMajor = Layout == <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(<a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a0103672ae41005ab03b4176c765afd62">ColMajor</a>);</div>
<div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160; </div>
<div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;    IndexType offset = 0;</div>
<div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;    DSizes&lt;IndexType, NumDims&gt; dimensions;</div>
<div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160; </div>
<div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;    <span class="keywordflow">if</span> (NumDims == 0) <span class="keywordflow">return</span> BlockDescriptor(offset, dimensions);</div>
<div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160; </div>
<div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;    <span class="comment">// Iterate outer -&gt; inner dimensions.</span></div>
<div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = NumDims - 1; i &gt;= 0; --i) {</div>
<div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">int</span> dim = isColMajor ? i : NumDims - i - 1;</div>
<div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160; </div>
<div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;      <span class="keyword">const</span> IndexType idx = block_index / m_block_strides[dim];</div>
<div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;      block_index -= idx * m_block_strides[dim];</div>
<div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160; </div>
<div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;      <span class="keyword">const</span> IndexType coord = idx * m_block_dimensions[dim];</div>
<div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;      dimensions[dim] = numext::mini(m_tensor_dimensions[dim] - coord,</div>
<div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;                                     m_block_dimensions[dim]);</div>
<div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;      offset += coord * m_tensor_strides[dim];</div>
<div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;    }</div>
<div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160; </div>
<div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;    <span class="keywordflow">return</span> {offset, dimensions};</div>
<div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;  }</div>
<div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160; </div>
<div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160; <span class="keyword">private</span>:</div>
<div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;  <span class="keywordtype">void</span> InitializeBlockDimensions() {</div>
<div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;    <span class="comment">// Requested block shape and size.</span></div>
<div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;    <span class="keyword">const</span> TensorBlockShapeType shape_type = m_requirements.shape_type;</div>
<div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;    IndexType target_block_size =</div>
<div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;        numext::maxi&lt;IndexType&gt;(1, <span class="keyword">static_cast&lt;</span>IndexType<span class="keyword">&gt;</span>(m_requirements.size));</div>
<div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160; </div>
<div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;    IndexType tensor_size = m_tensor_dimensions.TotalSize();</div>
<div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160; </div>
<div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;    <span class="comment">// Corner case: one of the dimensions is zero. Logic below is too complex</span></div>
<div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;    <span class="comment">// to handle this case on a general basis, just use unit block size.</span></div>
<div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;    <span class="comment">// Note: we must not yield blocks with zero dimensions (recipe for</span></div>
<div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;    <span class="comment">// overflows/underflows, divisions by zero and NaNs later).</span></div>
<div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;    <span class="keywordflow">if</span> (tensor_size == 0) {</div>
<div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; NumDims; ++i) {</div>
<div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;        m_block_dimensions[i] = 1;</div>
<div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;      }</div>
<div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;      m_total_block_count = 0;</div>
<div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;      <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;    }</div>
<div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160; </div>
<div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;    <span class="comment">// If tensor fits into a target block size, evaluate it as a single block.</span></div>
<div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;    <span class="keywordflow">if</span> (tensor_size &lt;= target_block_size) {</div>
<div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;      m_block_dimensions = m_tensor_dimensions;</div>
<div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;      m_total_block_count = 1;</div>
<div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;      <span class="comment">// The only valid block index is `0`, and in this case we do not need</span></div>
<div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;      <span class="comment">// to compute real strides for tensor or blocks (see blockDescriptor).</span></div>
<div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; NumDims; ++i) {</div>
<div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;        m_tensor_strides[i] = 0;</div>
<div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;        m_block_strides[i] = 1;</div>
<div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;      }</div>
<div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;      <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;    }</div>
<div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160; </div>
<div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;    <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> isColMajor = Layout == <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(<a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a0103672ae41005ab03b4176c765afd62">ColMajor</a>);</div>
<div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160; </div>
<div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;    <span class="comment">// Block shape skewed towards inner dimension.</span></div>
<div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;    <span class="keywordflow">if</span> (shape_type == TensorBlockShapeType::kSkewedInnerDims) {</div>
<div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;      IndexType coeff_to_allocate = target_block_size;</div>
<div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160; </div>
<div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; NumDims; ++i) {</div>
<div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span> dim = isColMajor ? i : NumDims - i - 1;</div>
<div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;        m_block_dimensions[dim] =</div>
<div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;            numext::mini(coeff_to_allocate, m_tensor_dimensions[dim]);</div>
<div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;        coeff_to_allocate = divup(</div>
<div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;            coeff_to_allocate,</div>
<div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;            numext::maxi(<span class="keyword">static_cast&lt;</span>IndexType<span class="keyword">&gt;</span>(1), m_block_dimensions[dim]));</div>
<div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;      }</div>
<div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;      eigen_assert(coeff_to_allocate == 1);</div>
<div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160; </div>
<div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;    } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (shape_type == TensorBlockShapeType::kUniformAllDims) {</div>
<div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;      <span class="comment">// Tensor will not fit within &#39;target_block_size&#39; budget: calculate tensor</span></div>
<div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;      <span class="comment">// block dimension sizes based on &quot;square&quot; dimension size target.</span></div>
<div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;      <span class="keyword">const</span> IndexType dim_size_target = convert_index&lt;IndexType&gt;(</div>
<div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;          std::pow(<span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(target_block_size),</div>
<div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;                   1.0f / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(m_block_dimensions.rank())));</div>
<div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160; </div>
<div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; NumDims; ++i) {</div>
<div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;        <span class="comment">// TODO(andydavis) Adjust the inner most &#39;block_dim_size&#39; to make it</span></div>
<div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;        <span class="comment">// a multiple of the packet size. Note that reducing</span></div>
<div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;        <span class="comment">// &#39;block_dim_size&#39; in this manner can increase the number of</span></div>
<div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;        <span class="comment">// blocks, and so will amplify any per-block overhead.</span></div>
<div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;        m_block_dimensions[i] =</div>
<div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;            numext::mini(dim_size_target, m_tensor_dimensions[i]);</div>
<div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;      }</div>
<div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160; </div>
<div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;      <span class="comment">// Add any un-allocated coefficients to inner dimension(s).</span></div>
<div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;      IndexType total_size = m_block_dimensions.TotalSize();</div>
<div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; NumDims; ++i) {</div>
<div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span> dim = isColMajor ? i : NumDims - i - 1;</div>
<div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160; </div>
<div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;        <span class="keywordflow">if</span> (m_block_dimensions[dim] &lt; m_tensor_dimensions[dim]) {</div>
<div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;          <span class="keyword">const</span> IndexType total_size_other_dims =</div>
<div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;              total_size / m_block_dimensions[dim];</div>
<div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;          <span class="keyword">const</span> IndexType alloc_avail =</div>
<div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;              divup&lt;IndexType&gt;(target_block_size, total_size_other_dims);</div>
<div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;          <span class="keywordflow">if</span> (alloc_avail == m_block_dimensions[dim]) {</div>
<div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;            <span class="comment">// Insufficient excess coefficients to allocate.</span></div>
<div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;            <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;          }</div>
<div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;          m_block_dimensions[dim] =</div>
<div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;              numext::mini(m_tensor_dimensions[dim], alloc_avail);</div>
<div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;          total_size = total_size_other_dims * m_block_dimensions[dim];</div>
<div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;        }</div>
<div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;      }</div>
<div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160; </div>
<div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;    } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;      eigen_assert(<span class="keyword">false</span>);  <span class="comment">// unknown block shape</span></div>
<div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;    }</div>
<div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160; </div>
<div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;    eigen_assert(m_block_dimensions.TotalSize() &gt;=</div>
<div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;                 numext::mini&lt;IndexType&gt;(target_block_size,</div>
<div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;                                         m_tensor_dimensions.TotalSize()));</div>
<div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160; </div>
<div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160;    <span class="comment">// Calculate block counts by dimension and total block count.</span></div>
<div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;    DSizes&lt;IndexType, NumDims&gt; block_count;</div>
<div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; NumDims; ++i) {</div>
<div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;      block_count[i] = divup(m_tensor_dimensions[i], m_block_dimensions[i]);</div>
<div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;    }</div>
<div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;    m_total_block_count = array_prod(block_count);</div>
<div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160; </div>
<div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;    <span class="comment">// Calculate block strides (used for enumerating blocks).</span></div>
<div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;    m_tensor_strides = strides&lt;Layout&gt;(m_tensor_dimensions);</div>
<div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160;    m_block_strides = strides&lt;Layout&gt;(block_count);</div>
<div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;  }</div>
<div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160; </div>
<div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;  DSizes&lt;IndexType, NumDims&gt; m_tensor_dimensions;</div>
<div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;  TensorBlockResourceRequirements m_requirements;</div>
<div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160; </div>
<div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;  DSizes&lt;IndexType, NumDims&gt; m_block_dimensions;</div>
<div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;  IndexType m_total_block_count;</div>
<div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160; </div>
<div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;  DSizes&lt;IndexType, NumDims&gt; m_tensor_strides;</div>
<div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;  DSizes&lt;IndexType, NumDims&gt; m_block_strides;</div>
<div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;};</div>
<div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160; </div>
<div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;<span class="comment">// -------------------------------------------------------------------------- //</span></div>
<div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;<span class="comment">// TensorBlockScratchAllocator is responsible for allocating temporary buffers</span></div>
<div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160;<span class="comment">// for block evaluation (output or input block materialization). Given that</span></div>
<div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;<span class="comment">// Eigen expression traversal order is deterministic, all temporary allocations</span></div>
<div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;<span class="comment">// are happening in the same order, and usually have exactly the same size.</span></div>
<div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;<span class="comment">// Scratch allocator keeps a trace of all dynamic allocations, and after the</span></div>
<div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;<span class="comment">// first block evaluation is completed, we should be able to reuse all the</span></div>
<div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;<span class="comment">// temporary buffers for the next block evaluation.</span></div>
<div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160; </div>
<div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Device&gt;</div>
<div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;<span class="keyword">class </span>TensorBlockScratchAllocator {</div>
<div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;  <span class="keyword">explicit</span> TensorBlockScratchAllocator(<span class="keyword">const</span> Device&amp; device)</div>
<div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;      : m_device(device), m_allocation_index(0) {}</div>
<div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160; </div>
<div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;  ~TensorBlockScratchAllocator() {</div>
<div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; m_allocations.size(); ++i) {</div>
<div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;      m_device.deallocate(m_allocations[i].ptr);</div>
<div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;    }</div>
<div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;  }</div>
<div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160; </div>
<div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;  <span class="keywordtype">void</span>* allocate(<span class="keywordtype">size_t</span> size) {</div>
<div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;    <span class="comment">// TODO(ezhulenev): Remove when replaced with inlined vector.</span></div>
<div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;    <span class="keywordflow">if</span> (m_allocations.capacity() == 0) m_allocations.reserve(8);</div>
<div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160; </div>
<div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;    <span class="comment">// Check if we already have an existing allocation att current index.</span></div>
<div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> num_allocations = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(m_allocations.size());</div>
<div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span> has_allocation = m_allocation_index &lt; num_allocations;</div>
<div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160; </div>
<div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;    <span class="comment">// Allocation index can&#39;t be larger than the number of allocations.</span></div>
<div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160;    eigen_assert(m_allocation_index &lt;= num_allocations);</div>
<div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160; </div>
<div class="line"><a name="l00549"></a><span class="lineno">  549</span>&#160;    <span class="comment">// If we have existing allocation, and its size is larger or equal to</span></div>
<div class="line"><a name="l00550"></a><span class="lineno">  550</span>&#160;    <span class="comment">// requested size, we do nothing.</span></div>
<div class="line"><a name="l00551"></a><span class="lineno">  551</span>&#160; </div>
<div class="line"><a name="l00552"></a><span class="lineno">  552</span>&#160;    <span class="comment">// If current allocation can&#39;t fit requested size, we deallocate it, and</span></div>
<div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;    <span class="comment">// replace with a larger allocation.</span></div>
<div class="line"><a name="l00554"></a><span class="lineno">  554</span>&#160;    <span class="keywordflow">if</span> (has_allocation &amp;&amp; m_allocations[m_allocation_index].size &lt; size) {</div>
<div class="line"><a name="l00555"></a><span class="lineno">  555</span>&#160;      m_device.deallocate(m_allocations[m_allocation_index].ptr);</div>
<div class="line"><a name="l00556"></a><span class="lineno">  556</span>&#160;      m_allocations[m_allocation_index].ptr = m_device.allocate(size);</div>
<div class="line"><a name="l00557"></a><span class="lineno">  557</span>&#160;      m_allocations[m_allocation_index].size = size;</div>
<div class="line"><a name="l00558"></a><span class="lineno">  558</span>&#160;    }</div>
<div class="line"><a name="l00559"></a><span class="lineno">  559</span>&#160; </div>
<div class="line"><a name="l00560"></a><span class="lineno">  560</span>&#160;    <span class="comment">// Make a new allocation if we don&#39;t have and existing one.</span></div>
<div class="line"><a name="l00561"></a><span class="lineno">  561</span>&#160;    <span class="keywordflow">if</span> (!has_allocation) {</div>
<div class="line"><a name="l00562"></a><span class="lineno">  562</span>&#160;      Allocation allocation;</div>
<div class="line"><a name="l00563"></a><span class="lineno">  563</span>&#160;      allocation.ptr = m_device.allocate(size);</div>
<div class="line"><a name="l00564"></a><span class="lineno">  564</span>&#160;      allocation.size = size;</div>
<div class="line"><a name="l00565"></a><span class="lineno">  565</span>&#160;      m_allocations.push_back(allocation);</div>
<div class="line"><a name="l00566"></a><span class="lineno">  566</span>&#160;    }</div>
<div class="line"><a name="l00567"></a><span class="lineno">  567</span>&#160; </div>
<div class="line"><a name="l00568"></a><span class="lineno">  568</span>&#160;    eigen_assert(m_allocations[m_allocation_index].ptr != NULL);</div>
<div class="line"><a name="l00569"></a><span class="lineno">  569</span>&#160;    eigen_assert(m_allocations[m_allocation_index].size &gt;= size);</div>
<div class="line"><a name="l00570"></a><span class="lineno">  570</span>&#160; </div>
<div class="line"><a name="l00571"></a><span class="lineno">  571</span>&#160;    <span class="keywordflow">return</span> m_allocations[m_allocation_index++].ptr;</div>
<div class="line"><a name="l00572"></a><span class="lineno">  572</span>&#160;  }</div>
<div class="line"><a name="l00573"></a><span class="lineno">  573</span>&#160; </div>
<div class="line"><a name="l00574"></a><span class="lineno">  574</span>&#160;  <span class="keywordtype">void</span> reset() { m_allocation_index = 0; }</div>
<div class="line"><a name="l00575"></a><span class="lineno">  575</span>&#160; </div>
<div class="line"><a name="l00576"></a><span class="lineno">  576</span>&#160; <span class="keyword">private</span>:</div>
<div class="line"><a name="l00577"></a><span class="lineno">  577</span>&#160;  <span class="keyword">struct </span>Allocation {</div>
<div class="line"><a name="l00578"></a><span class="lineno">  578</span>&#160;    <span class="keywordtype">void</span>* ptr;</div>
<div class="line"><a name="l00579"></a><span class="lineno">  579</span>&#160;    <span class="keywordtype">size_t</span> size;</div>
<div class="line"><a name="l00580"></a><span class="lineno">  580</span>&#160;  };</div>
<div class="line"><a name="l00581"></a><span class="lineno">  581</span>&#160; </div>
<div class="line"><a name="l00582"></a><span class="lineno">  582</span>&#160;  <span class="keyword">const</span> Device&amp; m_device;</div>
<div class="line"><a name="l00583"></a><span class="lineno">  583</span>&#160;  <span class="keywordtype">int</span> m_allocation_index;</div>
<div class="line"><a name="l00584"></a><span class="lineno">  584</span>&#160;  <span class="comment">// TODO(ezhulenev): This should be an inlined vector.</span></div>
<div class="line"><a name="l00585"></a><span class="lineno">  585</span>&#160;  std::vector&lt;Allocation&gt; m_allocations;</div>
<div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160;};</div>
<div class="line"><a name="l00587"></a><span class="lineno">  587</span>&#160; </div>
<div class="line"><a name="l00588"></a><span class="lineno">  588</span>&#160;<span class="comment">// -------------------------------------------------------------------------- //</span></div>
<div class="line"><a name="l00589"></a><span class="lineno">  589</span>&#160;<span class="comment">// TensorBlockKind represents all possible block kinds, that can be produced by</span></div>
<div class="line"><a name="l00590"></a><span class="lineno">  590</span>&#160;<span class="comment">// TensorEvaluator::evalBlock function.</span></div>
<div class="line"><a name="l00591"></a><span class="lineno">  591</span>&#160;<span class="keyword">enum</span> TensorBlockKind {</div>
<div class="line"><a name="l00592"></a><span class="lineno">  592</span>&#160;  <span class="comment">// Tensor block that is a lazy expression that must be assigned to a</span></div>
<div class="line"><a name="l00593"></a><span class="lineno">  593</span>&#160;  <span class="comment">// destination using TensorBlockAssign.</span></div>
<div class="line"><a name="l00594"></a><span class="lineno">  594</span>&#160;  kExpr,</div>
<div class="line"><a name="l00595"></a><span class="lineno">  595</span>&#160; </div>
<div class="line"><a name="l00596"></a><span class="lineno">  596</span>&#160;  <span class="comment">// Tensor block that is a view into a memory buffer owned by an underlying</span></div>
<div class="line"><a name="l00597"></a><span class="lineno">  597</span>&#160;  <span class="comment">// Tensor expression (e.g. it can be a view into a Tensor buffer).</span></div>
<div class="line"><a name="l00598"></a><span class="lineno">  598</span>&#160;  kView,</div>
<div class="line"><a name="l00599"></a><span class="lineno">  599</span>&#160; </div>
<div class="line"><a name="l00600"></a><span class="lineno">  600</span>&#160;  <span class="comment">// Tensor block that was materialized in a scratch memory buffer, allocated</span></div>
<div class="line"><a name="l00601"></a><span class="lineno">  601</span>&#160;  <span class="comment">// with TensorBlockScratchAllocator. This block must be copied to a</span></div>
<div class="line"><a name="l00602"></a><span class="lineno">  602</span>&#160;  <span class="comment">// destination, similar to a block of `kExpr` type.</span></div>
<div class="line"><a name="l00603"></a><span class="lineno">  603</span>&#160;  kMaterializedInScratch,</div>
<div class="line"><a name="l00604"></a><span class="lineno">  604</span>&#160; </div>
<div class="line"><a name="l00605"></a><span class="lineno">  605</span>&#160;  <span class="comment">// Tensor block that was materialized directly into the final output memory</span></div>
<div class="line"><a name="l00606"></a><span class="lineno">  606</span>&#160;  <span class="comment">// buffer. For example if the left side of an assignment is a Tensor, we can</span></div>
<div class="line"><a name="l00607"></a><span class="lineno">  607</span>&#160;  <span class="comment">// directly materialize the block in the destination memory.</span></div>
<div class="line"><a name="l00608"></a><span class="lineno">  608</span>&#160;  <span class="comment">//</span></div>
<div class="line"><a name="l00609"></a><span class="lineno">  609</span>&#160;  <span class="comment">// If strides in the output buffer do not match tensor block strides, the</span></div>
<div class="line"><a name="l00610"></a><span class="lineno">  610</span>&#160;  <span class="comment">// Tensor expression will be invalid, and should not be used by</span></div>
<div class="line"><a name="l00611"></a><span class="lineno">  611</span>&#160;  <span class="comment">// TensorBlockAssign or for constructing another block expression.</span></div>
<div class="line"><a name="l00612"></a><span class="lineno">  612</span>&#160;  kMaterializedInOutput</div>
<div class="line"><a name="l00613"></a><span class="lineno">  613</span>&#160;};</div>
<div class="line"><a name="l00614"></a><span class="lineno">  614</span>&#160; </div>
<div class="line"><a name="l00615"></a><span class="lineno">  615</span>&#160;<span class="comment">// -------------------------------------------------------------------------- //</span></div>
<div class="line"><a name="l00616"></a><span class="lineno">  616</span>&#160;<span class="comment">// TensorBlockNotImplemented should be used to defined TensorBlock typedef in</span></div>
<div class="line"><a name="l00617"></a><span class="lineno">  617</span>&#160;<span class="comment">// TensorEvaluators that do not support block evaluation.</span></div>
<div class="line"><a name="l00618"></a><span class="lineno">  618</span>&#160; </div>
<div class="line"><a name="l00619"></a><span class="lineno">  619</span>&#160;<span class="keyword">class </span>TensorBlockNotImplemented {</div>
<div class="line"><a name="l00620"></a><span class="lineno">  620</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l00621"></a><span class="lineno">  621</span>&#160;  <span class="keyword">typedef</span> <span class="keywordtype">void</span> XprType;</div>
<div class="line"><a name="l00622"></a><span class="lineno">  622</span>&#160;};</div>
<div class="line"><a name="l00623"></a><span class="lineno">  623</span>&#160; </div>
<div class="line"><a name="l00624"></a><span class="lineno">  624</span>&#160;<span class="comment">// -------------------------------------------------------------------------- //</span></div>
<div class="line"><a name="l00625"></a><span class="lineno">  625</span>&#160;<span class="comment">// XprScalar extracts Scalar type from the Eigen expressions (if expression type</span></div>
<div class="line"><a name="l00626"></a><span class="lineno">  626</span>&#160;<span class="comment">// is not void). It&#39;s required to be able to define lazy block expression for</span></div>
<div class="line"><a name="l00627"></a><span class="lineno">  627</span>&#160;<span class="comment">// argument types, that do not support block evaluation.</span></div>
<div class="line"><a name="l00628"></a><span class="lineno">  628</span>&#160; </div>
<div class="line"><a name="l00629"></a><span class="lineno">  629</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> XprType&gt;</div>
<div class="line"><a name="l00630"></a><span class="lineno">  630</span>&#160;<span class="keyword">struct </span>XprScalar {</div>
<div class="line"><a name="l00631"></a><span class="lineno">  631</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> XprType::Scalar type;</div>
<div class="line"><a name="l00632"></a><span class="lineno">  632</span>&#160;};</div>
<div class="line"><a name="l00633"></a><span class="lineno">  633</span>&#160;<span class="keyword">template</span> &lt;&gt;</div>
<div class="line"><a name="l00634"></a><span class="lineno">  634</span>&#160;<span class="keyword">struct </span>XprScalar&lt;void&gt; {</div>
<div class="line"><a name="l00635"></a><span class="lineno">  635</span>&#160;  <span class="keyword">typedef</span> <span class="keywordtype">void</span> type;</div>
<div class="line"><a name="l00636"></a><span class="lineno">  636</span>&#160;};</div>
<div class="line"><a name="l00637"></a><span class="lineno">  637</span>&#160; </div>
<div class="line"><a name="l00638"></a><span class="lineno">  638</span>&#160;<span class="comment">// -------------------------------------------------------------------------- //</span></div>
<div class="line"><a name="l00639"></a><span class="lineno">  639</span>&#160;<span class="comment">// TensorMaterializedBlock is a fully evaluated block of the original tensor,</span></div>
<div class="line"><a name="l00640"></a><span class="lineno">  640</span>&#160;<span class="comment">// and XprType is just a TensorMap over the data. This block type is typically</span></div>
<div class="line"><a name="l00641"></a><span class="lineno">  641</span>&#160;<span class="comment">// used to materialize blocks of tensor expressions, that can&#39;t be efficiently</span></div>
<div class="line"><a name="l00642"></a><span class="lineno">  642</span>&#160;<span class="comment">// represented as lazy Tensor expressions with fast coeff/packet operations,</span></div>
<div class="line"><a name="l00643"></a><span class="lineno">  643</span>&#160;<span class="comment">// e.g. we materialize all broadcasts into evaluated blocks.</span></div>
<div class="line"><a name="l00644"></a><span class="lineno">  644</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l00645"></a><span class="lineno">  645</span>&#160;<span class="comment">// TensorMaterializedBlock does not own its memory buffer, it&#39;s either a memory</span></div>
<div class="line"><a name="l00646"></a><span class="lineno">  646</span>&#160;<span class="comment">// buffer that backs the original expression (e.g. block is just a view into a</span></div>
<div class="line"><a name="l00647"></a><span class="lineno">  647</span>&#160;<span class="comment">// Tensor), or a memory buffer allocated with scratch allocator, and in this</span></div>
<div class="line"><a name="l00648"></a><span class="lineno">  648</span>&#160;<span class="comment">// case the scratch allocator will deallocate it at the end of block based</span></div>
<div class="line"><a name="l00649"></a><span class="lineno">  649</span>&#160;<span class="comment">// expression execution.</span></div>
<div class="line"><a name="l00650"></a><span class="lineno">  650</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l00651"></a><span class="lineno">  651</span>&#160;<span class="comment">// If the block was evaluated directly into the output buffer, and strides in</span></div>
<div class="line"><a name="l00652"></a><span class="lineno">  652</span>&#160;<span class="comment">// the output buffer do not match block strides, the TensorMap expression will</span></div>
<div class="line"><a name="l00653"></a><span class="lineno">  653</span>&#160;<span class="comment">// be invalid, and should never be used in block assignment or any other tensor</span></div>
<div class="line"><a name="l00654"></a><span class="lineno">  654</span>&#160;<span class="comment">// expression.</span></div>
<div class="line"><a name="l00655"></a><span class="lineno">  655</span>&#160; </div>
<div class="line"><a name="l00656"></a><span class="lineno">  656</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Scalar, <span class="keywordtype">int</span> NumDims, <span class="keywordtype">int</span> Layout,</div>
<div class="line"><a name="l00657"></a><span class="lineno">  657</span>&#160;          <span class="keyword">typename</span> IndexType = <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Eigen::Index</a>&gt;</div>
<div class="line"><a name="l00658"></a><span class="lineno">  658</span>&#160;<span class="keyword">class </span>TensorMaterializedBlock {</div>
<div class="line"><a name="l00659"></a><span class="lineno">  659</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l00660"></a><span class="lineno">  660</span>&#160;  <span class="keyword">typedef</span> DSizes&lt;IndexType, NumDims&gt; Dimensions;</div>
<div class="line"><a name="l00661"></a><span class="lineno">  661</span>&#160;  <span class="keyword">typedef</span> TensorMap&lt;const Tensor&lt;Scalar, NumDims, Layout&gt; &gt; XprType;</div>
<div class="line"><a name="l00662"></a><span class="lineno">  662</span>&#160; </div>
<div class="line"><a name="l00663"></a><span class="lineno">  663</span>&#160;  TensorMaterializedBlock(TensorBlockKind kind, <span class="keyword">const</span> Scalar* data,</div>
<div class="line"><a name="l00664"></a><span class="lineno">  664</span>&#160;                          <span class="keyword">const</span> Dimensions&amp; dimensions, <span class="keywordtype">bool</span> valid_expr = <span class="keyword">true</span>)</div>
<div class="line"><a name="l00665"></a><span class="lineno">  665</span>&#160;      : m_kind(kind),</div>
<div class="line"><a name="l00666"></a><span class="lineno">  666</span>&#160;        m_data(data),</div>
<div class="line"><a name="l00667"></a><span class="lineno">  667</span>&#160;        m_dimensions(dimensions),</div>
<div class="line"><a name="l00668"></a><span class="lineno">  668</span>&#160;        m_expr(m_data, m_dimensions),</div>
<div class="line"><a name="l00669"></a><span class="lineno">  669</span>&#160;        m_valid_expr(valid_expr) {</div>
<div class="line"><a name="l00670"></a><span class="lineno">  670</span>&#160;    eigen_assert(m_kind == internal::TensorBlockKind::kView ||</div>
<div class="line"><a name="l00671"></a><span class="lineno">  671</span>&#160;                 m_kind == internal::TensorBlockKind::kMaterializedInScratch ||</div>
<div class="line"><a name="l00672"></a><span class="lineno">  672</span>&#160;                 m_kind == internal::TensorBlockKind::kMaterializedInOutput);</div>
<div class="line"><a name="l00673"></a><span class="lineno">  673</span>&#160;  }</div>
<div class="line"><a name="l00674"></a><span class="lineno">  674</span>&#160; </div>
<div class="line"><a name="l00675"></a><span class="lineno">  675</span>&#160;  TensorBlockKind kind()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_kind; }</div>
<div class="line"><a name="l00676"></a><span class="lineno">  676</span>&#160;  <span class="comment">// NOTE(ezhulenev): Returning XprType by value like in other block types</span></div>
<div class="line"><a name="l00677"></a><span class="lineno">  677</span>&#160;  <span class="comment">// causes asan failures. The theory is that XprType::Nested doesn&#39;t work</span></div>
<div class="line"><a name="l00678"></a><span class="lineno">  678</span>&#160;  <span class="comment">// properly for TensorMap.</span></div>
<div class="line"><a name="l00679"></a><span class="lineno">  679</span>&#160;  <span class="keyword">const</span> XprType&amp; expr()<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00680"></a><span class="lineno">  680</span>&#160;    eigen_assert(m_valid_expr);</div>
<div class="line"><a name="l00681"></a><span class="lineno">  681</span>&#160;    <span class="keywordflow">return</span> m_expr;</div>
<div class="line"><a name="l00682"></a><span class="lineno">  682</span>&#160;  }</div>
<div class="line"><a name="l00683"></a><span class="lineno">  683</span>&#160;  <span class="keyword">const</span> Scalar* data()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_data; }</div>
<div class="line"><a name="l00684"></a><span class="lineno">  684</span>&#160;  <span class="keywordtype">void</span> cleanup() {}</div>
<div class="line"><a name="l00685"></a><span class="lineno">  685</span>&#160; </div>
<div class="line"><a name="l00686"></a><span class="lineno">  686</span>&#160;  <span class="keyword">typedef</span> internal::TensorBlockDescriptor&lt;NumDims, IndexType&gt; TensorBlockDesc;</div>
<div class="line"><a name="l00687"></a><span class="lineno">  687</span>&#160; </div>
<div class="line"><a name="l00688"></a><span class="lineno">  688</span>&#160;  <span class="comment">// TensorMaterializedBlock can be backed by different types of storage:</span></div>
<div class="line"><a name="l00689"></a><span class="lineno">  689</span>&#160;  <span class="comment">//</span></div>
<div class="line"><a name="l00690"></a><span class="lineno">  690</span>&#160;  <span class="comment">//   (1) Contiguous block of memory allocated with scratch allocator.</span></div>
<div class="line"><a name="l00691"></a><span class="lineno">  691</span>&#160;  <span class="comment">//   (2) Contiguous block of memory reused from tensor block descriptor</span></div>
<div class="line"><a name="l00692"></a><span class="lineno">  692</span>&#160;  <span class="comment">//       destination buffer.</span></div>
<div class="line"><a name="l00693"></a><span class="lineno">  693</span>&#160;  <span class="comment">//   (3) Strided block of memory reused from tensor block descriptor</span></div>
<div class="line"><a name="l00694"></a><span class="lineno">  694</span>&#160;  <span class="comment">//       destination buffer.</span></div>
<div class="line"><a name="l00695"></a><span class="lineno">  695</span>&#160;  <span class="comment">//</span></div>
<div class="line"><a name="l00696"></a><span class="lineno">  696</span>&#160;  <span class="keyword">class </span>Storage {</div>
<div class="line"><a name="l00697"></a><span class="lineno">  697</span>&#160;   <span class="keyword">public</span>:</div>
<div class="line"><a name="l00698"></a><span class="lineno">  698</span>&#160;    Scalar* data()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_data; }</div>
<div class="line"><a name="l00699"></a><span class="lineno">  699</span>&#160;    <span class="keyword">const</span> Dimensions&amp; dimensions()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_dimensions; }</div>
<div class="line"><a name="l00700"></a><span class="lineno">  700</span>&#160;    <span class="keyword">const</span> Dimensions&amp; strides()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_strides; }</div>
<div class="line"><a name="l00701"></a><span class="lineno">  701</span>&#160; </div>
<div class="line"><a name="l00702"></a><span class="lineno">  702</span>&#160;    TensorMaterializedBlock AsTensorMaterializedBlock()<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00703"></a><span class="lineno">  703</span>&#160;      <span class="keywordflow">return</span> TensorMaterializedBlock(</div>
<div class="line"><a name="l00704"></a><span class="lineno">  704</span>&#160;          m_materialized_in_output</div>
<div class="line"><a name="l00705"></a><span class="lineno">  705</span>&#160;              ? internal::TensorBlockKind::kMaterializedInOutput</div>
<div class="line"><a name="l00706"></a><span class="lineno">  706</span>&#160;              : internal::TensorBlockKind::kMaterializedInScratch,</div>
<div class="line"><a name="l00707"></a><span class="lineno">  707</span>&#160;          m_data, m_dimensions, !m_strided_storage);</div>
<div class="line"><a name="l00708"></a><span class="lineno">  708</span>&#160;    }</div>
<div class="line"><a name="l00709"></a><span class="lineno">  709</span>&#160; </div>
<div class="line"><a name="l00710"></a><span class="lineno">  710</span>&#160;   <span class="keyword">private</span>:</div>
<div class="line"><a name="l00711"></a><span class="lineno">  711</span>&#160;    <span class="keyword">friend</span> <span class="keyword">class </span>TensorMaterializedBlock&lt;Scalar, NumDims, Layout, IndexType&gt;;</div>
<div class="line"><a name="l00712"></a><span class="lineno">  712</span>&#160; </div>
<div class="line"><a name="l00713"></a><span class="lineno">  713</span>&#160;    Storage(Scalar* data, <span class="keyword">const</span> Dimensions&amp; dimensions,</div>
<div class="line"><a name="l00714"></a><span class="lineno">  714</span>&#160;            <span class="keyword">const</span> Dimensions&amp; strides, <span class="keywordtype">bool</span> materialized_in_output,</div>
<div class="line"><a name="l00715"></a><span class="lineno">  715</span>&#160;            <span class="keywordtype">bool</span> strided_storage)</div>
<div class="line"><a name="l00716"></a><span class="lineno">  716</span>&#160;        : m_data(data),</div>
<div class="line"><a name="l00717"></a><span class="lineno">  717</span>&#160;          m_dimensions(dimensions),</div>
<div class="line"><a name="l00718"></a><span class="lineno">  718</span>&#160;          m_strides(strides),</div>
<div class="line"><a name="l00719"></a><span class="lineno">  719</span>&#160;          m_materialized_in_output(materialized_in_output),</div>
<div class="line"><a name="l00720"></a><span class="lineno">  720</span>&#160;          m_strided_storage(strided_storage) {}</div>
<div class="line"><a name="l00721"></a><span class="lineno">  721</span>&#160; </div>
<div class="line"><a name="l00722"></a><span class="lineno">  722</span>&#160;    Scalar* m_data;</div>
<div class="line"><a name="l00723"></a><span class="lineno">  723</span>&#160;    Dimensions m_dimensions;</div>
<div class="line"><a name="l00724"></a><span class="lineno">  724</span>&#160;    Dimensions m_strides;</div>
<div class="line"><a name="l00725"></a><span class="lineno">  725</span>&#160;    <span class="keywordtype">bool</span> m_materialized_in_output;</div>
<div class="line"><a name="l00726"></a><span class="lineno">  726</span>&#160;    <span class="keywordtype">bool</span> m_strided_storage;</div>
<div class="line"><a name="l00727"></a><span class="lineno">  727</span>&#160;  };</div>
<div class="line"><a name="l00728"></a><span class="lineno">  728</span>&#160; </div>
<div class="line"><a name="l00729"></a><span class="lineno">  729</span>&#160;  <span class="comment">// Creates a storage for materialized block either from the block descriptor</span></div>
<div class="line"><a name="l00730"></a><span class="lineno">  730</span>&#160;  <span class="comment">// destination buffer, or allocates a new buffer with scratch allocator.</span></div>
<div class="line"><a name="l00731"></a><span class="lineno">  731</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> TensorBlockScratch&gt;</div>
<div class="line"><a name="l00732"></a><span class="lineno">  732</span>&#160;  EIGEN_STRONG_INLINE <span class="keyword">static</span> Storage prepareStorage(</div>
<div class="line"><a name="l00733"></a><span class="lineno">  733</span>&#160;      TensorBlockDesc&amp; desc, TensorBlockScratch&amp; scratch,</div>
<div class="line"><a name="l00734"></a><span class="lineno">  734</span>&#160;      <span class="keywordtype">bool</span> allow_strided_storage = <span class="keyword">false</span>) {</div>
<div class="line"><a name="l00735"></a><span class="lineno">  735</span>&#160;    <span class="comment">// Try to reuse destination as an output block buffer.</span></div>
<div class="line"><a name="l00736"></a><span class="lineno">  736</span>&#160;    <span class="keyword">typedef</span> <span class="keyword">typename</span> TensorBlockDesc::DestinationBuffer DestinationBuffer;</div>
<div class="line"><a name="l00737"></a><span class="lineno">  737</span>&#160; </div>
<div class="line"><a name="l00738"></a><span class="lineno">  738</span>&#160;    <span class="keywordflow">if</span> (desc.destination().kind() == DestinationBuffer::kContiguous) {</div>
<div class="line"><a name="l00739"></a><span class="lineno">  739</span>&#160;      Scalar* buffer = desc.destination().template data&lt;Scalar&gt;();</div>
<div class="line"><a name="l00740"></a><span class="lineno">  740</span>&#160;      desc.DropDestinationBuffer();</div>
<div class="line"><a name="l00741"></a><span class="lineno">  741</span>&#160;      <span class="keywordflow">return</span> Storage(buffer, desc.dimensions(),</div>
<div class="line"><a name="l00742"></a><span class="lineno">  742</span>&#160;                     internal::strides&lt;Layout&gt;(desc.dimensions()),</div>
<div class="line"><a name="l00743"></a><span class="lineno">  743</span>&#160;                     <span class="comment">/*materialized_in_output=*/</span><span class="keyword">true</span>,</div>
<div class="line"><a name="l00744"></a><span class="lineno">  744</span>&#160;                     <span class="comment">/*strided_storage=*/</span><span class="keyword">false</span>);</div>
<div class="line"><a name="l00745"></a><span class="lineno">  745</span>&#160; </div>
<div class="line"><a name="l00746"></a><span class="lineno">  746</span>&#160;    } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (desc.destination().kind() == DestinationBuffer::kStrided &amp;&amp;</div>
<div class="line"><a name="l00747"></a><span class="lineno">  747</span>&#160;               allow_strided_storage) {</div>
<div class="line"><a name="l00748"></a><span class="lineno">  748</span>&#160;      Scalar* buffer = desc.destination().template data&lt;Scalar&gt;();</div>
<div class="line"><a name="l00749"></a><span class="lineno">  749</span>&#160;      desc.DropDestinationBuffer();</div>
<div class="line"><a name="l00750"></a><span class="lineno">  750</span>&#160;      <span class="keywordflow">return</span> Storage(buffer, desc.dimensions(), desc.destination().strides(),</div>
<div class="line"><a name="l00751"></a><span class="lineno">  751</span>&#160;                     <span class="comment">/*materialized_in_output=*/</span><span class="keyword">true</span>, <span class="comment">/*strided_storage=*/</span><span class="keyword">true</span>);</div>
<div class="line"><a name="l00752"></a><span class="lineno">  752</span>&#160; </div>
<div class="line"><a name="l00753"></a><span class="lineno">  753</span>&#160;    } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00754"></a><span class="lineno">  754</span>&#160;      <span class="keywordtype">void</span>* mem = scratch.allocate(desc.size() * <span class="keyword">sizeof</span>(Scalar));</div>
<div class="line"><a name="l00755"></a><span class="lineno">  755</span>&#160;      <span class="keywordflow">return</span> Storage(<span class="keyword">static_cast&lt;</span>Scalar*<span class="keyword">&gt;</span>(mem), desc.dimensions(),</div>
<div class="line"><a name="l00756"></a><span class="lineno">  756</span>&#160;                     internal::strides&lt;Layout&gt;(desc.dimensions()),</div>
<div class="line"><a name="l00757"></a><span class="lineno">  757</span>&#160;                     <span class="comment">/*materialized_in_output=*/</span><span class="keyword">false</span>,</div>
<div class="line"><a name="l00758"></a><span class="lineno">  758</span>&#160;                     <span class="comment">/*strided_storage=*/</span><span class="keyword">false</span>);</div>
<div class="line"><a name="l00759"></a><span class="lineno">  759</span>&#160;    }</div>
<div class="line"><a name="l00760"></a><span class="lineno">  760</span>&#160;  }</div>
<div class="line"><a name="l00761"></a><span class="lineno">  761</span>&#160; </div>
<div class="line"><a name="l00762"></a><span class="lineno">  762</span>&#160;  <span class="comment">// Creates a materialized block for the given descriptor from a memory buffer.</span></div>
<div class="line"><a name="l00763"></a><span class="lineno">  763</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> DataDimensions, <span class="keyword">typename</span> TensorBlockScratch&gt;</div>
<div class="line"><a name="l00764"></a><span class="lineno">  764</span>&#160;  EIGEN_STRONG_INLINE <span class="keyword">static</span> TensorMaterializedBlock materialize(</div>
<div class="line"><a name="l00765"></a><span class="lineno">  765</span>&#160;      <span class="keyword">const</span> Scalar* data, <span class="keyword">const</span> DataDimensions&amp; data_dims,</div>
<div class="line"><a name="l00766"></a><span class="lineno">  766</span>&#160;      TensorBlockDesc&amp; desc, TensorBlockScratch&amp; scratch) {</div>
<div class="line"><a name="l00767"></a><span class="lineno">  767</span>&#160;    eigen_assert(array_size&lt;DataDimensions&gt;::value == desc.dimensions().size());</div>
<div class="line"><a name="l00768"></a><span class="lineno">  768</span>&#160; </div>
<div class="line"><a name="l00769"></a><span class="lineno">  769</span>&#160;    <span class="comment">// If a tensor block dimensions covers a contiguous block of the underlying</span></div>
<div class="line"><a name="l00770"></a><span class="lineno">  770</span>&#160;    <span class="comment">// memory, we can skip block buffer memory allocation, and construct a block</span></div>
<div class="line"><a name="l00771"></a><span class="lineno">  771</span>&#160;    <span class="comment">// from existing `data` memory buffer.</span></div>
<div class="line"><a name="l00772"></a><span class="lineno">  772</span>&#160;    <span class="comment">//</span></div>
<div class="line"><a name="l00773"></a><span class="lineno">  773</span>&#160;    <span class="comment">// Example: (RowMajor layout)</span></div>
<div class="line"><a name="l00774"></a><span class="lineno">  774</span>&#160;    <span class="comment">//   data_dims:          [11, 12, 13, 14]</span></div>
<div class="line"><a name="l00775"></a><span class="lineno">  775</span>&#160;    <span class="comment">//   desc.dimensions():  [1,   1,  3, 14]</span></div>
<div class="line"><a name="l00776"></a><span class="lineno">  776</span>&#160;    <span class="comment">//</span></div>
<div class="line"><a name="l00777"></a><span class="lineno">  777</span>&#160;    <span class="comment">// In this case we can construct a TensorBlock starting at</span></div>
<div class="line"><a name="l00778"></a><span class="lineno">  778</span>&#160;    <span class="comment">// `data + desc.offset()`, with a `desc.dimensions()` block sizes.</span></div>
<div class="line"><a name="l00779"></a><span class="lineno">  779</span>&#160;    <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> is_col_major = Layout == <a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a0103672ae41005ab03b4176c765afd62">ColMajor</a>;</div>
<div class="line"><a name="l00780"></a><span class="lineno">  780</span>&#160; </div>
<div class="line"><a name="l00781"></a><span class="lineno">  781</span>&#160;    <span class="comment">// Find out how many inner dimensions have a matching size.</span></div>
<div class="line"><a name="l00782"></a><span class="lineno">  782</span>&#160;    <span class="keywordtype">int</span> num_matching_inner_dims = 0;</div>
<div class="line"><a name="l00783"></a><span class="lineno">  783</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; NumDims; ++i) {</div>
<div class="line"><a name="l00784"></a><span class="lineno">  784</span>&#160;      <span class="keywordtype">int</span> dim = is_col_major ? i : NumDims - i - 1;</div>
<div class="line"><a name="l00785"></a><span class="lineno">  785</span>&#160;      <span class="keywordflow">if</span> (data_dims[dim] != desc.dimensions()[dim]) <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00786"></a><span class="lineno">  786</span>&#160;      ++num_matching_inner_dims;</div>
<div class="line"><a name="l00787"></a><span class="lineno">  787</span>&#160;    }</div>
<div class="line"><a name="l00788"></a><span class="lineno">  788</span>&#160; </div>
<div class="line"><a name="l00789"></a><span class="lineno">  789</span>&#160;    <span class="comment">// All the outer dimensions must be of size `1`, except a single dimension</span></div>
<div class="line"><a name="l00790"></a><span class="lineno">  790</span>&#160;    <span class="comment">// before the matching inner dimension (`3` in the example above).</span></div>
<div class="line"><a name="l00791"></a><span class="lineno">  791</span>&#160;    <span class="keywordtype">bool</span> can_use_direct_access = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00792"></a><span class="lineno">  792</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = num_matching_inner_dims + 1; i &lt; NumDims; ++i) {</div>
<div class="line"><a name="l00793"></a><span class="lineno">  793</span>&#160;      <span class="keywordtype">int</span> dim = is_col_major ? i : NumDims - i - 1;</div>
<div class="line"><a name="l00794"></a><span class="lineno">  794</span>&#160;      <span class="keywordflow">if</span> (desc.dimension(dim) != 1) {</div>
<div class="line"><a name="l00795"></a><span class="lineno">  795</span>&#160;        can_use_direct_access = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00796"></a><span class="lineno">  796</span>&#160;        <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00797"></a><span class="lineno">  797</span>&#160;      }</div>
<div class="line"><a name="l00798"></a><span class="lineno">  798</span>&#160;    }</div>
<div class="line"><a name="l00799"></a><span class="lineno">  799</span>&#160; </div>
<div class="line"><a name="l00800"></a><span class="lineno">  800</span>&#160;    <span class="keywordflow">if</span> (can_use_direct_access) {</div>
<div class="line"><a name="l00801"></a><span class="lineno">  801</span>&#160;      <span class="keyword">const</span> Scalar* block_start = data + desc.offset();</div>
<div class="line"><a name="l00802"></a><span class="lineno">  802</span>&#160;      <span class="keywordflow">return</span> TensorMaterializedBlock(internal::TensorBlockKind::kView,</div>
<div class="line"><a name="l00803"></a><span class="lineno">  803</span>&#160;                                     block_start, desc.dimensions());</div>
<div class="line"><a name="l00804"></a><span class="lineno">  804</span>&#160; </div>
<div class="line"><a name="l00805"></a><span class="lineno">  805</span>&#160;    } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00806"></a><span class="lineno">  806</span>&#160;      <span class="comment">// Reuse destination buffer or allocate new buffer with scratch allocator.</span></div>
<div class="line"><a name="l00807"></a><span class="lineno">  807</span>&#160;      <span class="keyword">const</span> Storage storage = prepareStorage(desc, scratch);</div>
<div class="line"><a name="l00808"></a><span class="lineno">  808</span>&#160; </div>
<div class="line"><a name="l00809"></a><span class="lineno">  809</span>&#160;      <span class="keyword">typedef</span> internal::TensorBlockIO&lt;Scalar, IndexType, NumDims, Layout&gt;</div>
<div class="line"><a name="l00810"></a><span class="lineno">  810</span>&#160;          TensorBlockIO;</div>
<div class="line"><a name="l00811"></a><span class="lineno">  811</span>&#160;      <span class="keyword">typedef</span> <span class="keyword">typename</span> TensorBlockIO::Dst TensorBlockIODst;</div>
<div class="line"><a name="l00812"></a><span class="lineno">  812</span>&#160;      <span class="keyword">typedef</span> <span class="keyword">typename</span> TensorBlockIO::Src TensorBlockIOSrc;</div>
<div class="line"><a name="l00813"></a><span class="lineno">  813</span>&#160; </div>
<div class="line"><a name="l00814"></a><span class="lineno">  814</span>&#160;      TensorBlockIOSrc src(internal::strides&lt;Layout&gt;(Dimensions(data_dims)),</div>
<div class="line"><a name="l00815"></a><span class="lineno">  815</span>&#160;                           data, desc.offset());</div>
<div class="line"><a name="l00816"></a><span class="lineno">  816</span>&#160;      TensorBlockIODst dst(storage.dimensions(), storage.strides(),</div>
<div class="line"><a name="l00817"></a><span class="lineno">  817</span>&#160;                           storage.data());</div>
<div class="line"><a name="l00818"></a><span class="lineno">  818</span>&#160; </div>
<div class="line"><a name="l00819"></a><span class="lineno">  819</span>&#160;      TensorBlockIO::Copy(dst, src);</div>
<div class="line"><a name="l00820"></a><span class="lineno">  820</span>&#160;      <span class="keywordflow">return</span> storage.AsTensorMaterializedBlock();</div>
<div class="line"><a name="l00821"></a><span class="lineno">  821</span>&#160;    }</div>
<div class="line"><a name="l00822"></a><span class="lineno">  822</span>&#160;  }</div>
<div class="line"><a name="l00823"></a><span class="lineno">  823</span>&#160; </div>
<div class="line"><a name="l00824"></a><span class="lineno">  824</span>&#160; <span class="keyword">private</span>:</div>
<div class="line"><a name="l00825"></a><span class="lineno">  825</span>&#160;  TensorBlockKind m_kind;</div>
<div class="line"><a name="l00826"></a><span class="lineno">  826</span>&#160;  <span class="keyword">const</span> Scalar* m_data;</div>
<div class="line"><a name="l00827"></a><span class="lineno">  827</span>&#160;  Dimensions m_dimensions;</div>
<div class="line"><a name="l00828"></a><span class="lineno">  828</span>&#160;  XprType m_expr;</div>
<div class="line"><a name="l00829"></a><span class="lineno">  829</span>&#160;  <span class="keywordtype">bool</span> m_valid_expr;</div>
<div class="line"><a name="l00830"></a><span class="lineno">  830</span>&#160;};</div>
<div class="line"><a name="l00831"></a><span class="lineno">  831</span>&#160; </div>
<div class="line"><a name="l00832"></a><span class="lineno">  832</span>&#160;<span class="comment">// -------------------------------------------------------------------------- //</span></div>
<div class="line"><a name="l00833"></a><span class="lineno">  833</span>&#160;<span class="comment">// TensorCwiseUnaryBlock is a lazy tensor expression block that applies UnaryOp</span></div>
<div class="line"><a name="l00834"></a><span class="lineno">  834</span>&#160;<span class="comment">// functor to the blocks produced by the underlying Tensor expression.</span></div>
<div class="line"><a name="l00835"></a><span class="lineno">  835</span>&#160; </div>
<div class="line"><a name="l00836"></a><span class="lineno">  836</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> UnaryOp, <span class="keyword">typename</span> ArgTensorBlock&gt;</div>
<div class="line"><a name="l00837"></a><span class="lineno">  837</span>&#160;<span class="keyword">class </span>TensorCwiseUnaryBlock {</div>
<div class="line"><a name="l00838"></a><span class="lineno">  838</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">bool</span> NoArgBlockAccess =</div>
<div class="line"><a name="l00839"></a><span class="lineno">  839</span>&#160;      internal::is_void&lt;typename ArgTensorBlock::XprType&gt;::value;</div>
<div class="line"><a name="l00840"></a><span class="lineno">  840</span>&#160; </div>
<div class="line"><a name="l00841"></a><span class="lineno">  841</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l00842"></a><span class="lineno">  842</span>&#160;  <span class="keyword">typedef</span> std::conditional_t&lt;</div>
<div class="line"><a name="l00843"></a><span class="lineno">  843</span>&#160;      NoArgBlockAccess, void,</div>
<div class="line"><a name="l00844"></a><span class="lineno">  844</span>&#160;      TensorCwiseUnaryOp&lt;UnaryOp, const typename ArgTensorBlock::XprType&gt; &gt;</div>
<div class="line"><a name="l00845"></a><span class="lineno">  845</span>&#160;      XprType;</div>
<div class="line"><a name="l00846"></a><span class="lineno">  846</span>&#160; </div>
<div class="line"><a name="l00847"></a><span class="lineno">  847</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> XprScalar&lt;XprType&gt;::type Scalar;</div>
<div class="line"><a name="l00848"></a><span class="lineno">  848</span>&#160; </div>
<div class="line"><a name="l00849"></a><span class="lineno">  849</span>&#160;  TensorCwiseUnaryBlock(<span class="keyword">const</span> ArgTensorBlock&amp; arg_block, <span class="keyword">const</span> UnaryOp&amp; functor)</div>
<div class="line"><a name="l00850"></a><span class="lineno">  850</span>&#160;      : m_arg_block(arg_block), m_functor(functor) {}</div>
<div class="line"><a name="l00851"></a><span class="lineno">  851</span>&#160; </div>
<div class="line"><a name="l00852"></a><span class="lineno">  852</span>&#160;  TensorBlockKind kind()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> internal::TensorBlockKind::kExpr; }</div>
<div class="line"><a name="l00853"></a><span class="lineno">  853</span>&#160; </div>
<div class="line"><a name="l00854"></a><span class="lineno">  854</span>&#160;  XprType expr()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> XprType(m_arg_block.expr(), m_functor); }</div>
<div class="line"><a name="l00855"></a><span class="lineno">  855</span>&#160;  <span class="keyword">const</span> Scalar* data()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> NULL; }</div>
<div class="line"><a name="l00856"></a><span class="lineno">  856</span>&#160;  <span class="keywordtype">void</span> cleanup() { m_arg_block.cleanup(); }</div>
<div class="line"><a name="l00857"></a><span class="lineno">  857</span>&#160; </div>
<div class="line"><a name="l00858"></a><span class="lineno">  858</span>&#160; <span class="keyword">private</span>:</div>
<div class="line"><a name="l00859"></a><span class="lineno">  859</span>&#160;  ArgTensorBlock m_arg_block;</div>
<div class="line"><a name="l00860"></a><span class="lineno">  860</span>&#160;  UnaryOp m_functor;</div>
<div class="line"><a name="l00861"></a><span class="lineno">  861</span>&#160;};</div>
<div class="line"><a name="l00862"></a><span class="lineno">  862</span>&#160; </div>
<div class="line"><a name="l00863"></a><span class="lineno">  863</span>&#160;<span class="comment">// -------------------------------------------------------------------------- //</span></div>
<div class="line"><a name="l00864"></a><span class="lineno">  864</span>&#160;<span class="comment">// TensorCwiseUnaryBlock is a lazy tensor expression block that applies BinaryOp</span></div>
<div class="line"><a name="l00865"></a><span class="lineno">  865</span>&#160;<span class="comment">// functor to the blocks produced by the underlying Tensor expression.</span></div>
<div class="line"><a name="l00866"></a><span class="lineno">  866</span>&#160; </div>
<div class="line"><a name="l00867"></a><span class="lineno">  867</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> BinaryOp, <span class="keyword">typename</span> LhsTensorBlock, <span class="keyword">typename</span> RhsTensorBlock&gt;</div>
<div class="line"><a name="l00868"></a><span class="lineno">  868</span>&#160;<span class="keyword">class </span>TensorCwiseBinaryBlock {</div>
<div class="line"><a name="l00869"></a><span class="lineno">  869</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">bool</span> NoArgBlockAccess =</div>
<div class="line"><a name="l00870"></a><span class="lineno">  870</span>&#160;      internal::is_void&lt;typename LhsTensorBlock::XprType&gt;::value ||</div>
<div class="line"><a name="l00871"></a><span class="lineno">  871</span>&#160;      internal::is_void&lt;typename RhsTensorBlock::XprType&gt;::value;</div>
<div class="line"><a name="l00872"></a><span class="lineno">  872</span>&#160; </div>
<div class="line"><a name="l00873"></a><span class="lineno">  873</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l00874"></a><span class="lineno">  874</span>&#160;  <span class="keyword">typedef</span> std::conditional_t&lt;</div>
<div class="line"><a name="l00875"></a><span class="lineno">  875</span>&#160;      NoArgBlockAccess, void,</div>
<div class="line"><a name="l00876"></a><span class="lineno">  876</span>&#160;      TensorCwiseBinaryOp&lt;BinaryOp, <span class="keyword">const</span> <span class="keyword">typename</span> LhsTensorBlock::XprType,</div>
<div class="line"><a name="l00877"></a><span class="lineno">  877</span>&#160;                          <span class="keyword">const</span> <span class="keyword">typename</span> RhsTensorBlock::XprType&gt; &gt;</div>
<div class="line"><a name="l00878"></a><span class="lineno">  878</span>&#160;      XprType;</div>
<div class="line"><a name="l00879"></a><span class="lineno">  879</span>&#160; </div>
<div class="line"><a name="l00880"></a><span class="lineno">  880</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> XprScalar&lt;XprType&gt;::type Scalar;</div>
<div class="line"><a name="l00881"></a><span class="lineno">  881</span>&#160; </div>
<div class="line"><a name="l00882"></a><span class="lineno">  882</span>&#160;  TensorCwiseBinaryBlock(<span class="keyword">const</span> LhsTensorBlock&amp; left_block,</div>
<div class="line"><a name="l00883"></a><span class="lineno">  883</span>&#160;                         <span class="keyword">const</span> RhsTensorBlock&amp; right_block,</div>
<div class="line"><a name="l00884"></a><span class="lineno">  884</span>&#160;                         <span class="keyword">const</span> BinaryOp&amp; functor)</div>
<div class="line"><a name="l00885"></a><span class="lineno">  885</span>&#160;      : m_left_block(left_block),</div>
<div class="line"><a name="l00886"></a><span class="lineno">  886</span>&#160;        m_right_block(right_block),</div>
<div class="line"><a name="l00887"></a><span class="lineno">  887</span>&#160;        m_functor(functor) {}</div>
<div class="line"><a name="l00888"></a><span class="lineno">  888</span>&#160; </div>
<div class="line"><a name="l00889"></a><span class="lineno">  889</span>&#160;  TensorBlockKind kind()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> internal::TensorBlockKind::kExpr; }</div>
<div class="line"><a name="l00890"></a><span class="lineno">  890</span>&#160; </div>
<div class="line"><a name="l00891"></a><span class="lineno">  891</span>&#160;  XprType expr()<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00892"></a><span class="lineno">  892</span>&#160;    <span class="keywordflow">return</span> XprType(m_left_block.expr(), m_right_block.expr(), m_functor);</div>
<div class="line"><a name="l00893"></a><span class="lineno">  893</span>&#160;  }</div>
<div class="line"><a name="l00894"></a><span class="lineno">  894</span>&#160; </div>
<div class="line"><a name="l00895"></a><span class="lineno">  895</span>&#160;  <span class="keyword">const</span> Scalar* data()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> NULL; }</div>
<div class="line"><a name="l00896"></a><span class="lineno">  896</span>&#160; </div>
<div class="line"><a name="l00897"></a><span class="lineno">  897</span>&#160;  <span class="keywordtype">void</span> cleanup() {</div>
<div class="line"><a name="l00898"></a><span class="lineno">  898</span>&#160;    m_left_block.cleanup();</div>
<div class="line"><a name="l00899"></a><span class="lineno">  899</span>&#160;    m_right_block.cleanup();</div>
<div class="line"><a name="l00900"></a><span class="lineno">  900</span>&#160;  }</div>
<div class="line"><a name="l00901"></a><span class="lineno">  901</span>&#160; </div>
<div class="line"><a name="l00902"></a><span class="lineno">  902</span>&#160; <span class="keyword">private</span>:</div>
<div class="line"><a name="l00903"></a><span class="lineno">  903</span>&#160;  LhsTensorBlock m_left_block;</div>
<div class="line"><a name="l00904"></a><span class="lineno">  904</span>&#160;  RhsTensorBlock m_right_block;</div>
<div class="line"><a name="l00905"></a><span class="lineno">  905</span>&#160;  BinaryOp m_functor;</div>
<div class="line"><a name="l00906"></a><span class="lineno">  906</span>&#160;};</div>
<div class="line"><a name="l00907"></a><span class="lineno">  907</span>&#160; </div>
<div class="line"><a name="l00908"></a><span class="lineno">  908</span>&#160;<span class="comment">// -------------------------------------------------------------------------- //</span></div>
<div class="line"><a name="l00909"></a><span class="lineno">  909</span>&#160;<span class="comment">// TensorUnaryExprBlock is a lazy tensor expression block that can construct</span></div>
<div class="line"><a name="l00910"></a><span class="lineno">  910</span>&#160;<span class="comment">// an arbitrary tensor expression from a block of the underlying type (this is a</span></div>
<div class="line"><a name="l00911"></a><span class="lineno">  911</span>&#160;<span class="comment">// generalization of the TensorCwiseUnaryBlock for arbitrary expressions).</span></div>
<div class="line"><a name="l00912"></a><span class="lineno">  912</span>&#160; </div>
<div class="line"><a name="l00913"></a><span class="lineno">  913</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> BlockFactory, <span class="keyword">typename</span> ArgTensorBlock&gt;</div>
<div class="line"><a name="l00914"></a><span class="lineno">  914</span>&#160;<span class="keyword">class </span>TensorUnaryExprBlock {</div>
<div class="line"><a name="l00915"></a><span class="lineno">  915</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> ArgTensorBlock::XprType ArgXprType;</div>
<div class="line"><a name="l00916"></a><span class="lineno">  916</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">bool</span> NoArgBlockAccess = internal::is_void&lt;ArgXprType&gt;::value;</div>
<div class="line"><a name="l00917"></a><span class="lineno">  917</span>&#160; </div>
<div class="line"><a name="l00918"></a><span class="lineno">  918</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l00919"></a><span class="lineno">  919</span>&#160;  <span class="keyword">typedef</span> std::conditional_t&lt;</div>
<div class="line"><a name="l00920"></a><span class="lineno">  920</span>&#160;      NoArgBlockAccess, void,</div>
<div class="line"><a name="l00921"></a><span class="lineno">  921</span>&#160;      <span class="keyword">typename</span> BlockFactory::template XprType&lt;ArgXprType&gt;::type&gt; XprType;</div>
<div class="line"><a name="l00922"></a><span class="lineno">  922</span>&#160; </div>
<div class="line"><a name="l00923"></a><span class="lineno">  923</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> XprScalar&lt;XprType&gt;::type Scalar;</div>
<div class="line"><a name="l00924"></a><span class="lineno">  924</span>&#160; </div>
<div class="line"><a name="l00925"></a><span class="lineno">  925</span>&#160;  TensorUnaryExprBlock(<span class="keyword">const</span> ArgTensorBlock&amp; arg_block,</div>
<div class="line"><a name="l00926"></a><span class="lineno">  926</span>&#160;                       <span class="keyword">const</span> BlockFactory&amp; factory)</div>
<div class="line"><a name="l00927"></a><span class="lineno">  927</span>&#160;      : m_arg_block(arg_block), m_factory(factory) {}</div>
<div class="line"><a name="l00928"></a><span class="lineno">  928</span>&#160; </div>
<div class="line"><a name="l00929"></a><span class="lineno">  929</span>&#160;  TensorBlockKind kind()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> internal::TensorBlockKind::kExpr; }</div>
<div class="line"><a name="l00930"></a><span class="lineno">  930</span>&#160;  XprType expr()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_factory.expr(m_arg_block.expr()); }</div>
<div class="line"><a name="l00931"></a><span class="lineno">  931</span>&#160;  <span class="keyword">const</span> Scalar* data()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> NULL; }</div>
<div class="line"><a name="l00932"></a><span class="lineno">  932</span>&#160;  <span class="keywordtype">void</span> cleanup() { m_arg_block.cleanup(); }</div>
<div class="line"><a name="l00933"></a><span class="lineno">  933</span>&#160; </div>
<div class="line"><a name="l00934"></a><span class="lineno">  934</span>&#160; <span class="keyword">private</span>:</div>
<div class="line"><a name="l00935"></a><span class="lineno">  935</span>&#160;  ArgTensorBlock m_arg_block;</div>
<div class="line"><a name="l00936"></a><span class="lineno">  936</span>&#160;  BlockFactory m_factory;</div>
<div class="line"><a name="l00937"></a><span class="lineno">  937</span>&#160;};</div>
<div class="line"><a name="l00938"></a><span class="lineno">  938</span>&#160; </div>
<div class="line"><a name="l00939"></a><span class="lineno">  939</span>&#160;<span class="comment">// -------------------------------------------------------------------------- //</span></div>
<div class="line"><a name="l00940"></a><span class="lineno">  940</span>&#160;<span class="comment">// TensorTernaryExprBlock is a lazy tensor expression block that can construct</span></div>
<div class="line"><a name="l00941"></a><span class="lineno">  941</span>&#160;<span class="comment">// an arbitrary tensor expression from three blocks of the underlying type.</span></div>
<div class="line"><a name="l00942"></a><span class="lineno">  942</span>&#160; </div>
<div class="line"><a name="l00943"></a><span class="lineno">  943</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> BlockFactory, <span class="keyword">typename</span> Arg1TensorBlock,</div>
<div class="line"><a name="l00944"></a><span class="lineno">  944</span>&#160;          <span class="keyword">typename</span> Arg2TensorBlock, <span class="keyword">typename</span> Arg3TensorBlock&gt;</div>
<div class="line"><a name="l00945"></a><span class="lineno">  945</span>&#160;<span class="keyword">class </span>TensorTernaryExprBlock {</div>
<div class="line"><a name="l00946"></a><span class="lineno">  946</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> Arg1TensorBlock::XprType Arg1XprType;</div>
<div class="line"><a name="l00947"></a><span class="lineno">  947</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> Arg2TensorBlock::XprType Arg2XprType;</div>
<div class="line"><a name="l00948"></a><span class="lineno">  948</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> Arg3TensorBlock::XprType Arg3XprType;</div>
<div class="line"><a name="l00949"></a><span class="lineno">  949</span>&#160; </div>
<div class="line"><a name="l00950"></a><span class="lineno">  950</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">bool</span> NoArgBlockAccess = internal::is_void&lt;Arg1XprType&gt;::value ||</div>
<div class="line"><a name="l00951"></a><span class="lineno">  951</span>&#160;                                           internal::is_void&lt;Arg2XprType&gt;::value ||</div>
<div class="line"><a name="l00952"></a><span class="lineno">  952</span>&#160;                                           internal::is_void&lt;Arg3XprType&gt;::value;</div>
<div class="line"><a name="l00953"></a><span class="lineno">  953</span>&#160; </div>
<div class="line"><a name="l00954"></a><span class="lineno">  954</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l00955"></a><span class="lineno">  955</span>&#160;  <span class="keyword">typedef</span> std::conditional_t&lt;</div>
<div class="line"><a name="l00956"></a><span class="lineno">  956</span>&#160;      NoArgBlockAccess, void,</div>
<div class="line"><a name="l00957"></a><span class="lineno">  957</span>&#160;      <span class="keyword">typename</span> BlockFactory::template XprType&lt;Arg1XprType, Arg2XprType,</div>
<div class="line"><a name="l00958"></a><span class="lineno">  958</span>&#160;                                              Arg3XprType&gt;::type&gt; XprType;</div>
<div class="line"><a name="l00959"></a><span class="lineno">  959</span>&#160; </div>
<div class="line"><a name="l00960"></a><span class="lineno">  960</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> XprScalar&lt;XprType&gt;::type Scalar;</div>
<div class="line"><a name="l00961"></a><span class="lineno">  961</span>&#160; </div>
<div class="line"><a name="l00962"></a><span class="lineno">  962</span>&#160;  TensorTernaryExprBlock(<span class="keyword">const</span> Arg1TensorBlock&amp; arg1_block,</div>
<div class="line"><a name="l00963"></a><span class="lineno">  963</span>&#160;                         <span class="keyword">const</span> Arg2TensorBlock&amp; arg2_block,</div>
<div class="line"><a name="l00964"></a><span class="lineno">  964</span>&#160;                         <span class="keyword">const</span> Arg3TensorBlock&amp; arg3_block,</div>
<div class="line"><a name="l00965"></a><span class="lineno">  965</span>&#160;                         <span class="keyword">const</span> BlockFactory&amp; factory)</div>
<div class="line"><a name="l00966"></a><span class="lineno">  966</span>&#160;      : m_arg1_block(arg1_block),</div>
<div class="line"><a name="l00967"></a><span class="lineno">  967</span>&#160;        m_arg2_block(arg2_block),</div>
<div class="line"><a name="l00968"></a><span class="lineno">  968</span>&#160;        m_arg3_block(arg3_block),</div>
<div class="line"><a name="l00969"></a><span class="lineno">  969</span>&#160;        m_factory(factory) {}</div>
<div class="line"><a name="l00970"></a><span class="lineno">  970</span>&#160; </div>
<div class="line"><a name="l00971"></a><span class="lineno">  971</span>&#160;  TensorBlockKind kind()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> internal::TensorBlockKind::kExpr; }</div>
<div class="line"><a name="l00972"></a><span class="lineno">  972</span>&#160;  XprType expr()<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00973"></a><span class="lineno">  973</span>&#160;    <span class="keywordflow">return</span> m_factory.expr(m_arg1_block.expr(), m_arg2_block.expr(),</div>
<div class="line"><a name="l00974"></a><span class="lineno">  974</span>&#160;                          m_arg3_block.expr());</div>
<div class="line"><a name="l00975"></a><span class="lineno">  975</span>&#160;  }</div>
<div class="line"><a name="l00976"></a><span class="lineno">  976</span>&#160;  <span class="keyword">const</span> Scalar* data()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> NULL; }</div>
<div class="line"><a name="l00977"></a><span class="lineno">  977</span>&#160;  <span class="keywordtype">void</span> cleanup() {</div>
<div class="line"><a name="l00978"></a><span class="lineno">  978</span>&#160;    m_arg1_block.cleanup();</div>
<div class="line"><a name="l00979"></a><span class="lineno">  979</span>&#160;    m_arg2_block.cleanup();</div>
<div class="line"><a name="l00980"></a><span class="lineno">  980</span>&#160;    m_arg3_block.cleanup();</div>
<div class="line"><a name="l00981"></a><span class="lineno">  981</span>&#160;  }</div>
<div class="line"><a name="l00982"></a><span class="lineno">  982</span>&#160; </div>
<div class="line"><a name="l00983"></a><span class="lineno">  983</span>&#160; <span class="keyword">private</span>:</div>
<div class="line"><a name="l00984"></a><span class="lineno">  984</span>&#160;  Arg1TensorBlock m_arg1_block;</div>
<div class="line"><a name="l00985"></a><span class="lineno">  985</span>&#160;  Arg2TensorBlock m_arg2_block;</div>
<div class="line"><a name="l00986"></a><span class="lineno">  986</span>&#160;  Arg3TensorBlock m_arg3_block;</div>
<div class="line"><a name="l00987"></a><span class="lineno">  987</span>&#160;  BlockFactory m_factory;</div>
<div class="line"><a name="l00988"></a><span class="lineno">  988</span>&#160;};</div>
<div class="line"><a name="l00989"></a><span class="lineno">  989</span>&#160; </div>
<div class="line"><a name="l00990"></a><span class="lineno">  990</span>&#160;<span class="comment">// -------------------------------------------------------------------------- //</span></div>
<div class="line"><a name="l00991"></a><span class="lineno">  991</span>&#160;<span class="comment">// StridedLinearBufferCopy provides a method to copy data between two linear</span></div>
<div class="line"><a name="l00992"></a><span class="lineno">  992</span>&#160;<span class="comment">// buffers with different strides, with optimized paths for scatter/gather.</span></div>
<div class="line"><a name="l00993"></a><span class="lineno">  993</span>&#160; </div>
<div class="line"><a name="l00994"></a><span class="lineno">  994</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Scalar, <span class="keyword">typename</span> IndexType&gt;</div>
<div class="line"><a name="l00995"></a><span class="lineno">  995</span>&#160;<span class="keyword">class </span>StridedLinearBufferCopy {</div>
<div class="line"><a name="l00996"></a><span class="lineno">  996</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> packet_traits&lt;Scalar&gt;::type Packet;</div>
<div class="line"><a name="l00997"></a><span class="lineno">  997</span>&#160;  <span class="keyword">enum</span> {</div>
<div class="line"><a name="l00998"></a><span class="lineno">  998</span>&#160;    Vectorizable = packet_traits&lt;Scalar&gt;::Vectorizable,</div>
<div class="line"><a name="l00999"></a><span class="lineno">  999</span>&#160;    PacketSize = packet_traits&lt;Scalar&gt;::size</div>
<div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>&#160;  };</div>
<div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>&#160; </div>
<div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>&#160;  <span class="comment">// Specifying linear copy kind statically gives ~30% speedup for small sizes.</span></div>
<div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>&#160;  <span class="keyword">enum class</span> Kind {</div>
<div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>&#160;    Linear = 0,       <span class="comment">// src_stride == 1 &amp;&amp; dst_stride == 1</span></div>
<div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>&#160;    Scatter = 1,      <span class="comment">// src_stride == 1 &amp;&amp; dst_stride != 1</span></div>
<div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>&#160;    FillLinear = 2,   <span class="comment">// src_stride == 0 &amp;&amp; dst_stride == 1</span></div>
<div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>&#160;    FillScatter = 3,  <span class="comment">// src_stride == 0 &amp;&amp; dst_stride != 1</span></div>
<div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>&#160;    Gather = 4,       <span class="comment">// dst_stride == 1</span></div>
<div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>&#160;    Random = 5        <span class="comment">// everything else</span></div>
<div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>&#160;  };</div>
<div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>&#160; </div>
<div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>&#160;  <span class="keyword">struct </span>Dst {</div>
<div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>&#160;    Dst(IndexType o, IndexType s, Scalar* d) : offset(o), stride(s), data(d) {}</div>
<div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>&#160; </div>
<div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>&#160;    IndexType offset;</div>
<div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>&#160;    IndexType stride;</div>
<div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>&#160;    Scalar* data;</div>
<div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>&#160;  };</div>
<div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>&#160; </div>
<div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>&#160;  <span class="keyword">struct </span>Src {</div>
<div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>&#160;    Src(IndexType o, IndexType s, <span class="keyword">const</span> Scalar* d)</div>
<div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>&#160;        : offset(o), stride(s), data(d) {}</div>
<div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>&#160; </div>
<div class="line"><a name="l01025"></a><span class="lineno"> 1025</span>&#160;    IndexType offset;</div>
<div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>&#160;    IndexType stride;</div>
<div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>&#160;    <span class="keyword">const</span> Scalar* data;</div>
<div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>&#160;  };</div>
<div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>&#160; </div>
<div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Str<span class="keywordtype">id</span>edLinearBufferCopy::Kind kind&gt;</div>
<div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>&#160;  <span class="keyword">static</span> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE <span class="keywordtype">void</span> Run(<span class="keyword">const</span> Dst&amp; dst,</div>
<div class="line"><a name="l01032"></a><span class="lineno"> 1032</span>&#160;                                                        <span class="keyword">const</span> Src&amp; src,</div>
<div class="line"><a name="l01033"></a><span class="lineno"> 1033</span>&#160;                                                        <span class="keyword">const</span> <span class="keywordtype">size_t</span> count) {</div>
<div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>&#160;    Run&lt;kind&gt;(count, dst.offset, dst.stride, dst.data, src.offset, src.stride,</div>
<div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>&#160;              src.data);</div>
<div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>&#160;  }</div>
<div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>&#160; </div>
<div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>&#160; <span class="keyword">private</span>:</div>
<div class="line"><a name="l01039"></a><span class="lineno"> 1039</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Str<span class="keywordtype">id</span>edLinearBufferCopy::Kind kind&gt;</div>
<div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>&#160;  <span class="keyword">static</span> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE <span class="keywordtype">void</span> Run(</div>
<div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>&#160;      <span class="keyword">const</span> IndexType count, <span class="keyword">const</span> IndexType dst_offset,</div>
<div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>&#160;      <span class="keyword">const</span> IndexType dst_stride, Scalar* EIGEN_RESTRICT dst_data,</div>
<div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>&#160;      <span class="keyword">const</span> IndexType src_offset, <span class="keyword">const</span> IndexType src_stride,</div>
<div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>&#160;      <span class="keyword">const</span> Scalar* EIGEN_RESTRICT src_data) {</div>
<div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>&#160;    <span class="keyword">const</span> Scalar* src = &amp;src_data[src_offset];</div>
<div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>&#160;    Scalar* dst = &amp;dst_data[dst_offset];</div>
<div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>&#160; </div>
<div class="line"><a name="l01048"></a><span class="lineno"> 1048</span>&#160;    <span class="keywordflow">if</span> (!Vectorizable) {</div>
<div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>&#160;      <span class="keywordflow">for</span> (<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> i = 0; i &lt; count; ++i) {</div>
<div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>&#160;        dst[i * dst_stride] = src[i * src_stride];</div>
<div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>&#160;      }</div>
<div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>&#160;      <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>&#160;    }</div>
<div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>&#160; </div>
<div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>&#160;    <span class="keyword">const</span> IndexType vectorized_size = count - PacketSize;</div>
<div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>&#160;    IndexType i = 0;</div>
<div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>&#160; </div>
<div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>&#160;    <span class="keywordflow">if</span> (kind == StridedLinearBufferCopy::Kind::Linear) {</div>
<div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>&#160;      <span class="comment">// ******************************************************************** //</span></div>
<div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>&#160;      <span class="comment">// Linear copy from `src` to `dst`.</span></div>
<div class="line"><a name="l01061"></a><span class="lineno"> 1061</span>&#160;      <span class="keyword">const</span> IndexType unrolled_size = count - 4 * PacketSize;</div>
<div class="line"><a name="l01062"></a><span class="lineno"> 1062</span>&#160;      eigen_assert(src_stride == 1 &amp;&amp; dst_stride == 1);</div>
<div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>&#160;      <span class="keywordflow">for</span> (; i &lt;= unrolled_size; i += 4 * PacketSize) {</div>
<div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; 4; ++j) {</div>
<div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>&#160;          Packet p = ploadu&lt;Packet&gt;(src + i + j * PacketSize);</div>
<div class="line"><a name="l01066"></a><span class="lineno"> 1066</span>&#160;          pstoreu&lt;Scalar, Packet&gt;(dst + i + j * PacketSize, p);</div>
<div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>&#160;        }</div>
<div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>&#160;      }</div>
<div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>&#160;      <span class="keywordflow">for</span> (; i &lt;= vectorized_size; i += PacketSize) {</div>
<div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>&#160;        Packet p = ploadu&lt;Packet&gt;(src + i);</div>
<div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>&#160;        pstoreu&lt;Scalar, Packet&gt;(dst + i, p);</div>
<div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>&#160;      }</div>
<div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>&#160;      <span class="keywordflow">for</span> (; i &lt; count; ++i) {</div>
<div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>&#160;        dst[i] = src[i];</div>
<div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>&#160;      }</div>
<div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>&#160;      <span class="comment">// ******************************************************************** //</span></div>
<div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>&#160;    } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (kind == StridedLinearBufferCopy::Kind::Scatter) {</div>
<div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>&#160;      <span class="comment">// Scatter from `src` to `dst`.</span></div>
<div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>&#160;      eigen_assert(src_stride == 1 &amp;&amp; dst_stride != 1);</div>
<div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>&#160;      <span class="keywordflow">for</span> (; i &lt;= vectorized_size; i += PacketSize) {</div>
<div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>&#160;        Packet p = ploadu&lt;Packet&gt;(src + i);</div>
<div class="line"><a name="l01082"></a><span class="lineno"> 1082</span>&#160;        pscatter&lt;Scalar, Packet&gt;(dst + i * dst_stride, p, dst_stride);</div>
<div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>&#160;      }</div>
<div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>&#160;      <span class="keywordflow">for</span> (; i &lt; count; ++i) {</div>
<div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>&#160;        dst[i * dst_stride] = src[i];</div>
<div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>&#160;      }</div>
<div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>&#160;      <span class="comment">// ******************************************************************** //</span></div>
<div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>&#160;    } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (kind == StridedLinearBufferCopy::Kind::FillLinear) {</div>
<div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>&#160;      <span class="comment">// Fill `dst` with value at `*src`.</span></div>
<div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>&#160;      eigen_assert(src_stride == 0 &amp;&amp; dst_stride == 1);</div>
<div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>&#160;      <span class="keyword">const</span> IndexType unrolled_size = count - 4 * PacketSize;</div>
<div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>&#160;      Packet p = pload1&lt;Packet&gt;(src);</div>
<div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>&#160;      <span class="keywordflow">for</span> (; i &lt;= unrolled_size; i += 4 * PacketSize) {</div>
<div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; 4; ++j) {</div>
<div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>&#160;          pstoreu&lt;Scalar, Packet&gt;(dst + i + j * PacketSize, p);</div>
<div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>&#160;        }</div>
<div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>&#160;      }</div>
<div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>&#160;      <span class="keywordflow">for</span> (; i &lt;= vectorized_size; i += PacketSize) {</div>
<div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>&#160;        pstoreu&lt;Scalar, Packet&gt;(dst + i, p);</div>
<div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>&#160;      }</div>
<div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>&#160;      <span class="keywordflow">for</span> (; i &lt; count; ++i) {</div>
<div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>&#160;        dst[i] = *src;</div>
<div class="line"><a name="l01103"></a><span class="lineno"> 1103</span>&#160;      }</div>
<div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>&#160;      <span class="comment">// ******************************************************************** //</span></div>
<div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>&#160;    } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (kind == StridedLinearBufferCopy::Kind::FillScatter) {</div>
<div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>&#160;      <span class="comment">// Scatter `*src` into `dst`.</span></div>
<div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>&#160;      eigen_assert(src_stride == 0 &amp;&amp; dst_stride != 1);</div>
<div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>&#160;      Packet p = pload1&lt;Packet&gt;(src);</div>
<div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>&#160;      <span class="keywordflow">for</span> (; i &lt;= vectorized_size; i += PacketSize) {</div>
<div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>&#160;        pscatter&lt;Scalar, Packet&gt;(dst + i * dst_stride, p, dst_stride);</div>
<div class="line"><a name="l01111"></a><span class="lineno"> 1111</span>&#160;      }</div>
<div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>&#160;      <span class="keywordflow">for</span> (; i &lt; count; ++i) {</div>
<div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>&#160;        dst[i * dst_stride] = *src;</div>
<div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>&#160;      }</div>
<div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>&#160;      <span class="comment">// ******************************************************************** //</span></div>
<div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>&#160;    } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (kind == StridedLinearBufferCopy::Kind::Gather) {</div>
<div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>&#160;      <span class="comment">// Gather from `src` into `dst`.</span></div>
<div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>&#160;      eigen_assert(dst_stride == 1);</div>
<div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>&#160;      <span class="keywordflow">for</span> (; i &lt;= vectorized_size; i += PacketSize) {</div>
<div class="line"><a name="l01120"></a><span class="lineno"> 1120</span>&#160;        Packet p = pgather&lt;Scalar, Packet&gt;(src + i * src_stride, src_stride);</div>
<div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>&#160;        pstoreu&lt;Scalar, Packet&gt;(dst + i, p);</div>
<div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>&#160;      }</div>
<div class="line"><a name="l01123"></a><span class="lineno"> 1123</span>&#160;      <span class="keywordflow">for</span> (; i &lt; count; ++i) {</div>
<div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>&#160;        dst[i] = src[i * src_stride];</div>
<div class="line"><a name="l01125"></a><span class="lineno"> 1125</span>&#160;      }</div>
<div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>&#160;      <span class="comment">// ******************************************************************** //</span></div>
<div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>&#160;    } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (kind == StridedLinearBufferCopy::Kind::Random) {</div>
<div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>&#160;      <span class="comment">// Random.</span></div>
<div class="line"><a name="l01129"></a><span class="lineno"> 1129</span>&#160;      <span class="keywordflow">for</span> (; i &lt; count; ++i) {</div>
<div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>&#160;        dst[i * dst_stride] = src[i * src_stride];</div>
<div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>&#160;      }</div>
<div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>&#160;    } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>&#160;      eigen_assert(<span class="keyword">false</span>);</div>
<div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>&#160;    }</div>
<div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>&#160;  }</div>
<div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>&#160;};</div>
<div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>&#160; </div>
<div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>&#160;<span class="comment">// -------------------------------------------------------------------------- //</span></div>
<div class="line"><a name="l01139"></a><span class="lineno"> 1139</span>&#160;<span class="comment">// TensorBlockIO copies data from `src` tensor block, to the `dst` tensor block.</span></div>
<div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>&#160;<span class="comment">// It&#39;s possible to specify src-&gt;dst dimension mapping for the copy operation.</span></div>
<div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>&#160;<span class="comment">// Dimensions of `dst` specify how many elements have to be copied, for the</span></div>
<div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>&#160;<span class="comment">// `src` we need to know only stride to navigate through source memory buffer.</span></div>
<div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>&#160; </div>
<div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Scalar, <span class="keyword">typename</span> IndexType, <span class="keywordtype">int</span> NumDims, <span class="keywordtype">int</span> Layout&gt;</div>
<div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>&#160;<span class="keyword">class </span>TensorBlockIO {</div>
<div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">bool</span> IsColMajor = (Layout == <a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a0103672ae41005ab03b4176c765afd62">ColMajor</a>);</div>
<div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>&#160; </div>
<div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>&#160;  <span class="keyword">typedef</span> StridedLinearBufferCopy&lt;Scalar, IndexType&gt; LinCopy;</div>
<div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>&#160; </div>
<div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>&#160;  <span class="keyword">typedef</span> DSizes&lt;IndexType, NumDims&gt; Dimensions;</div>
<div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>&#160;  <span class="keyword">typedef</span> DSizes&lt;int, NumDims&gt; DimensionsMap;</div>
<div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>&#160; </div>
<div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>&#160;  <span class="keyword">struct </span>Dst {</div>
<div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>&#160;    Dst(<span class="keyword">const</span> Dimensions&amp; dst_dims, <span class="keyword">const</span> Dimensions&amp; dst_strides, Scalar* dst,</div>
<div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>&#160;        IndexType dst_offset = 0)</div>
<div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>&#160;        : dims(dst_dims), strides(dst_strides), data(dst), offset(dst_offset) {}</div>
<div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>&#160; </div>
<div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>&#160;    Dimensions dims;</div>
<div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>&#160;    Dimensions strides;</div>
<div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>&#160;    Scalar* data;</div>
<div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>&#160;    IndexType offset;</div>
<div class="line"><a name="l01163"></a><span class="lineno"> 1163</span>&#160;  };</div>
<div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>&#160; </div>
<div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>&#160;  <span class="keyword">struct </span>Src {</div>
<div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>&#160;    Src(<span class="keyword">const</span> Dimensions&amp; src_strides, <span class="keyword">const</span> Scalar* src,</div>
<div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>&#160;        IndexType src_offset = 0)</div>
<div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>&#160;        : strides(src_strides), data(src), offset(src_offset) {}</div>
<div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>&#160; </div>
<div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>&#160;    Dimensions strides;</div>
<div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>&#160;    <span class="keyword">const</span> Scalar* data;</div>
<div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>&#160;    IndexType offset;</div>
<div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>&#160;  };</div>
<div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>&#160; </div>
<div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>&#160;  <span class="comment">// Copies data to `dst` from `src`, using provided dimensions mapping:</span></div>
<div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>&#160;  <span class="comment">//</span></div>
<div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>&#160;  <span class="comment">//   src_dimension_index = dst_to_src_dim_map[dst_dimension_index]</span></div>
<div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>&#160;  <span class="comment">//</span></div>
<div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>&#160;  <span class="comment">// Returns the number of copied elements.</span></div>
<div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>&#160;  <span class="keyword">static</span> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE IndexType Copy(</div>
<div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>&#160;      <span class="keyword">const</span> Dst&amp; dst, <span class="keyword">const</span> Src&amp; src, <span class="keyword">const</span> DimensionsMap&amp; dst_to_src_dim_map) {</div>
<div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>&#160;    <span class="comment">// Copy single scalar value from `src` to `dst`.</span></div>
<div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>&#160;    <span class="keywordflow">if</span> (NumDims == 0) {</div>
<div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>&#160;      *(dst.data + dst.offset) = *(src.data + src.offset);</div>
<div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>&#160;      <span class="keywordflow">return</span> 1;</div>
<div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>&#160;    }</div>
<div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>&#160; </div>
<div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>&#160;    <span class="comment">// Both `dst` and `src` must have contiguous innermost dimension. We also</span></div>
<div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>&#160;    <span class="comment">// accept the special case with stride &#39;0&#39;, because it&#39;s used as a trick to</span></div>
<div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>&#160;    <span class="comment">// implement broadcasting.</span></div>
<div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>&#160;    {</div>
<div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>&#160;      <span class="keywordtype">int</span> inner_dim = IsColMajor ? 0 : NumDims - 1;</div>
<div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>&#160;      EIGEN_UNUSED_VARIABLE(inner_dim);</div>
<div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>&#160;      eigen_assert(dst.strides[inner_dim] == 1 || dst.strides[inner_dim] == 0);</div>
<div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>&#160;      eigen_assert(src.strides[inner_dim] == 1 || src.strides[inner_dim] == 0);</div>
<div class="line"><a name="l01196"></a><span class="lineno"> 1196</span>&#160;    }</div>
<div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>&#160; </div>
<div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>&#160;    <span class="comment">// Give a shorter name to `dst_to_src_dim_map`.</span></div>
<div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>&#160;    <span class="keyword">const</span> DimensionsMap&amp; dim_map = dst_to_src_dim_map;</div>
<div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>&#160; </div>
<div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>&#160;    <span class="comment">// Do not squeeze reordered inner dimensions.</span></div>
<div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>&#160;    <span class="keywordtype">int</span> num_squeezable_dims = NumSqueezableInnerDims(dim_map);</div>
<div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>&#160; </div>
<div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>&#160;    <span class="comment">// NOTE: We find the innermost dimension (contiguous in memory) in the dst</span></div>
<div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>&#160;    <span class="comment">// block, and we write data linearly into that dimension, reading it from</span></div>
<div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>&#160;    <span class="comment">// the src. If dimensions are reordered, we might end up reading data from</span></div>
<div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>&#160;    <span class="comment">// the src with `stride != 1`.</span></div>
<div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>&#160;    <span class="comment">//</span></div>
<div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>&#160;    <span class="comment">// NOTE: Random-Read/Linear-Write can be up to ~2X faster than</span></div>
<div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>&#160;    <span class="comment">// Linear-Read/Random-Write: https://stackoverflow.com/a/54935680</span></div>
<div class="line"><a name="l01211"></a><span class="lineno"> 1211</span>&#160; </div>
<div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>&#160;    <span class="comment">// Find the innermost dimension in the dst whose size is not 1. This is the</span></div>
<div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>&#160;    <span class="comment">// effective inner dim.</span></div>
<div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>&#160;    <span class="keywordtype">int</span> num_size_one_inner_dims = 0;</div>
<div class="line"><a name="l01215"></a><span class="lineno"> 1215</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; num_squeezable_dims; ++i) {</div>
<div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">int</span> dst_dim = IsColMajor ? i : NumDims - i - 1;</div>
<div class="line"><a name="l01217"></a><span class="lineno"> 1217</span>&#160;      <span class="keywordflow">if</span> (dst.dims[dst_dim] != 1) <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l01218"></a><span class="lineno"> 1218</span>&#160;      num_size_one_inner_dims++;</div>
<div class="line"><a name="l01219"></a><span class="lineno"> 1219</span>&#160;    }</div>
<div class="line"><a name="l01220"></a><span class="lineno"> 1220</span>&#160; </div>
<div class="line"><a name="l01221"></a><span class="lineno"> 1221</span>&#160;    <span class="comment">// If all dimensions are of size 1, just copy a scalar from `src` to `dst`.</span></div>
<div class="line"><a name="l01222"></a><span class="lineno"> 1222</span>&#160;    <span class="keywordflow">if</span> (num_size_one_inner_dims == NumDims) {</div>
<div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>&#160;      *(dst.data + dst.offset) = *(src.data + src.offset);</div>
<div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>&#160;      <span class="keywordflow">return</span> 1;</div>
<div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>&#160;    }</div>
<div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>&#160; </div>
<div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>&#160;    <span class="comment">// Outermost dimension in the dst with `stride == 1` (contiguous in memory).</span></div>
<div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> dst_stride1_dim = IsColMajor</div>
<div class="line"><a name="l01229"></a><span class="lineno"> 1229</span>&#160;                                    ? num_size_one_inner_dims</div>
<div class="line"><a name="l01230"></a><span class="lineno"> 1230</span>&#160;                                    : NumDims - num_size_one_inner_dims - 1;</div>
<div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>&#160; </div>
<div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>&#160;    <span class="comment">// Dimension in the src that corresponds to the dst innermost dimension.</span></div>
<div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> src_dim_for_dst_stride1_dim =</div>
<div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>&#160;        NumDims == 0 ? 1 : dim_map[dst_stride1_dim];</div>
<div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>&#160; </div>
<div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>&#160;    <span class="comment">// Size of the innermost dimension (length of contiguous blocks of memory).</span></div>
<div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>&#160;    IndexType dst_inner_dim_size = NumDims == 0 ? 1 : dst.dims[dst_stride1_dim];</div>
<div class="line"><a name="l01238"></a><span class="lineno"> 1238</span>&#160; </div>
<div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>&#160;    <span class="comment">// Squeeze multiple inner dims into one if they are contiguous in `dst` and</span></div>
<div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>&#160;    <span class="comment">// `src` memory, so we can do less linear copy calls.</span></div>
<div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = num_size_one_inner_dims + 1; i &lt; num_squeezable_dims; ++i) {</div>
<div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">int</span> dst_dim = IsColMajor ? i : NumDims - i - 1;</div>
<div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>&#160;      <span class="keyword">const</span> IndexType dst_stride = dst.strides[dst_dim];</div>
<div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>&#160;      <span class="keyword">const</span> IndexType src_stride = src.strides[dim_map[dst_dim]];</div>
<div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>&#160;      <span class="keywordflow">if</span> (dst_inner_dim_size == dst_stride &amp;&amp; dst_stride == src_stride) {</div>
<div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>&#160;        dst_inner_dim_size *= dst.dims[dst_dim];</div>
<div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>&#160;        ++num_size_one_inner_dims;</div>
<div class="line"><a name="l01248"></a><span class="lineno"> 1248</span>&#160;      } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>&#160;        <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>&#160;      }</div>
<div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>&#160;    }</div>
<div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>&#160; </div>
<div class="line"><a name="l01253"></a><span class="lineno"> 1253</span>&#160;    <span class="comment">// Setup strides to read data from `src` and write to `dst`.</span></div>
<div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>&#160;    IndexType input_offset = src.offset;</div>
<div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>&#160;    IndexType output_offset = dst.offset;</div>
<div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>&#160;    IndexType input_stride =</div>
<div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>&#160;        NumDims == 0 ? 1 : src.strides[src_dim_for_dst_stride1_dim];</div>
<div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>&#160;    IndexType output_stride = NumDims == 0 ? 1 : dst.strides[dst_stride1_dim];</div>
<div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>&#160; </div>
<div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> at_least_1_dim = NumDims &lt;= 1 ? 1 : NumDims - 1;</div>
<div class="line"><a name="l01261"></a><span class="lineno"> 1261</span>&#160;    array&lt;BlockIteratorState, at_least_1_dim&gt; it;</div>
<div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>&#160; </div>
<div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>&#160;    <span class="comment">// Initialize block iterator state. Squeeze away any dimension of size 1.</span></div>
<div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>&#160;    <span class="keywordtype">int</span> idx = 0;  <span class="comment">// currently initialized iterator state index</span></div>
<div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = num_size_one_inner_dims; i &lt; NumDims - 1; ++i) {</div>
<div class="line"><a name="l01266"></a><span class="lineno"> 1266</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">int</span> dst_dim = IsColMajor ? i + 1 : NumDims - i - 2;</div>
<div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>&#160;      <span class="keywordflow">if</span> (dst.dims[dst_dim] == 1) <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l01268"></a><span class="lineno"> 1268</span>&#160; </div>
<div class="line"><a name="l01269"></a><span class="lineno"> 1269</span>&#160;      it[idx].size = dst.dims[dst_dim];</div>
<div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>&#160;      it[idx].input_stride = src.strides[dim_map[dst_dim]];</div>
<div class="line"><a name="l01271"></a><span class="lineno"> 1271</span>&#160;      it[idx].output_stride = dst.strides[dst_dim];</div>
<div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>&#160; </div>
<div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>&#160;      it[idx].input_span = it[idx].input_stride * (it[idx].size - 1);</div>
<div class="line"><a name="l01274"></a><span class="lineno"> 1274</span>&#160;      it[idx].output_span = it[idx].output_stride * (it[idx].size - 1);</div>
<div class="line"><a name="l01275"></a><span class="lineno"> 1275</span>&#160; </div>
<div class="line"><a name="l01276"></a><span class="lineno"> 1276</span>&#160;      idx++;</div>
<div class="line"><a name="l01277"></a><span class="lineno"> 1277</span>&#160;    }</div>
<div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>&#160; </div>
<div class="line"><a name="l01279"></a><span class="lineno"> 1279</span>&#160;    <span class="comment">// Iterate copying data from src to dst.</span></div>
<div class="line"><a name="l01280"></a><span class="lineno"> 1280</span>&#160;    <span class="keyword">const</span> IndexType block_total_size = NumDims == 0 ? 1 : dst.dims.TotalSize();</div>
<div class="line"><a name="l01281"></a><span class="lineno"> 1281</span>&#160; </div>
<div class="line"><a name="l01282"></a><span class="lineno"> 1282</span>&#160;<span class="preprocessor">#define COPY_INNER_DIM(KIND)                                           \</span></div>
<div class="line"><a name="l01283"></a><span class="lineno"> 1283</span>&#160;<span class="preprocessor">  IndexType num_copied = 0;                                            \</span></div>
<div class="line"><a name="l01284"></a><span class="lineno"> 1284</span>&#160;<span class="preprocessor">  for (num_copied = 0; num_copied &lt; block_total_size;                  \</span></div>
<div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>&#160;<span class="preprocessor">       num_copied += dst_inner_dim_size) {                             \</span></div>
<div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>&#160;<span class="preprocessor">    LinCopy::template Run&lt;KIND&gt;(                                       \</span></div>
<div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>&#160;<span class="preprocessor">        typename LinCopy::Dst(output_offset, output_stride, dst.data), \</span></div>
<div class="line"><a name="l01288"></a><span class="lineno"> 1288</span>&#160;<span class="preprocessor">        typename LinCopy::Src(input_offset, input_stride, src.data),   \</span></div>
<div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>&#160;<span class="preprocessor">        dst_inner_dim_size);                                           \</span></div>
<div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>&#160;<span class="preprocessor">                                                                       \</span></div>
<div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>&#160;<span class="preprocessor">    for (int j = 0; j &lt; idx; ++j) {                                    \</span></div>
<div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>&#160;<span class="preprocessor">      if (++it[j].count &lt; it[j].size) {                                \</span></div>
<div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>&#160;<span class="preprocessor">        input_offset += it[j].input_stride;                            \</span></div>
<div class="line"><a name="l01294"></a><span class="lineno"> 1294</span>&#160;<span class="preprocessor">        output_offset += it[j].output_stride;                          \</span></div>
<div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>&#160;<span class="preprocessor">        break;                                                         \</span></div>
<div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>&#160;<span class="preprocessor">      }                                                                \</span></div>
<div class="line"><a name="l01297"></a><span class="lineno"> 1297</span>&#160;<span class="preprocessor">      it[j].count = 0;                                                 \</span></div>
<div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>&#160;<span class="preprocessor">      input_offset -= it[j].input_span;                                \</span></div>
<div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>&#160;<span class="preprocessor">      output_offset -= it[j].output_span;                              \</span></div>
<div class="line"><a name="l01300"></a><span class="lineno"> 1300</span>&#160;<span class="preprocessor">    }                                                                  \</span></div>
<div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>&#160;<span class="preprocessor">  }                                                                    \</span></div>
<div class="line"><a name="l01302"></a><span class="lineno"> 1302</span>&#160;<span class="preprocessor">  return num_copied;</span></div>
<div class="line"><a name="l01303"></a><span class="lineno"> 1303</span>&#160; </div>
<div class="line"><a name="l01304"></a><span class="lineno"> 1304</span>&#160;    <span class="keywordflow">if</span> (input_stride == 1 &amp;&amp; output_stride == 1) {</div>
<div class="line"><a name="l01305"></a><span class="lineno"> 1305</span>&#160;      COPY_INNER_DIM(LinCopy::Kind::Linear);</div>
<div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>&#160;    } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (input_stride == 1 &amp;&amp; output_stride != 1) {</div>
<div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>&#160;      COPY_INNER_DIM(LinCopy::Kind::Scatter);</div>
<div class="line"><a name="l01308"></a><span class="lineno"> 1308</span>&#160;    } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (input_stride == 0 &amp;&amp; output_stride == 1) {</div>
<div class="line"><a name="l01309"></a><span class="lineno"> 1309</span>&#160;      COPY_INNER_DIM(LinCopy::Kind::FillLinear);</div>
<div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>&#160;    } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (input_stride == 0 &amp;&amp; output_stride != 1) {</div>
<div class="line"><a name="l01311"></a><span class="lineno"> 1311</span>&#160;      COPY_INNER_DIM(LinCopy::Kind::FillScatter);</div>
<div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>&#160;    } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (output_stride == 1) {</div>
<div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>&#160;      COPY_INNER_DIM(LinCopy::Kind::Gather);</div>
<div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>&#160;    } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l01315"></a><span class="lineno"> 1315</span>&#160;      COPY_INNER_DIM(LinCopy::Kind::Random);</div>
<div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>&#160;    }</div>
<div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>&#160; </div>
<div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>&#160;<span class="preprocessor">#undef COPY_INNER_DIM</span></div>
<div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>&#160;  }</div>
<div class="line"><a name="l01320"></a><span class="lineno"> 1320</span>&#160; </div>
<div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>&#160;  <span class="comment">// Copy from `src` to `dst` with an identity src-&gt;dst dimension map. Returns</span></div>
<div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>&#160;  <span class="comment">// the number of copied elements.</span></div>
<div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>&#160;  <span class="keyword">static</span> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE IndexType Copy(<span class="keyword">const</span> Dst&amp; dst,</div>
<div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>&#160;                                                              <span class="keyword">const</span> Src&amp; src) {</div>
<div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>&#160;    DimensionsMap dst_to_src_map;</div>
<div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; NumDims; ++i) dst_to_src_map[i] = i;</div>
<div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>&#160;    <span class="keywordflow">return</span> Copy(dst, src, dst_to_src_map);</div>
<div class="line"><a name="l01328"></a><span class="lineno"> 1328</span>&#160;  }</div>
<div class="line"><a name="l01329"></a><span class="lineno"> 1329</span>&#160; </div>
<div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>&#160; <span class="keyword">private</span>:</div>
<div class="line"><a name="l01331"></a><span class="lineno"> 1331</span>&#160;  <span class="keyword">struct </span>BlockIteratorState {</div>
<div class="line"><a name="l01332"></a><span class="lineno"> 1332</span>&#160;    BlockIteratorState()</div>
<div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>&#160;        : size(0),</div>
<div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>&#160;          count(0),</div>
<div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>&#160;          input_stride(0),</div>
<div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>&#160;          output_stride(0),</div>
<div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>&#160;          input_span(0),</div>
<div class="line"><a name="l01338"></a><span class="lineno"> 1338</span>&#160;          output_span(0) {}</div>
<div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>&#160; </div>
<div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>&#160;    IndexType size;</div>
<div class="line"><a name="l01341"></a><span class="lineno"> 1341</span>&#160;    IndexType count;</div>
<div class="line"><a name="l01342"></a><span class="lineno"> 1342</span>&#160;    IndexType input_stride;</div>
<div class="line"><a name="l01343"></a><span class="lineno"> 1343</span>&#160;    IndexType output_stride;</div>
<div class="line"><a name="l01344"></a><span class="lineno"> 1344</span>&#160;    IndexType input_span;</div>
<div class="line"><a name="l01345"></a><span class="lineno"> 1345</span>&#160;    IndexType output_span;</div>
<div class="line"><a name="l01346"></a><span class="lineno"> 1346</span>&#160;  };</div>
<div class="line"><a name="l01347"></a><span class="lineno"> 1347</span>&#160; </div>
<div class="line"><a name="l01348"></a><span class="lineno"> 1348</span>&#160;  <span class="comment">// Compute how many inner dimensions it&#39;s allowed to squeeze when doing IO</span></div>
<div class="line"><a name="l01349"></a><span class="lineno"> 1349</span>&#160;  <span class="comment">// between two tensor blocks. It&#39;s safe to squeeze inner dimensions, only</span></div>
<div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>&#160;  <span class="comment">// if they are not reordered.</span></div>
<div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>&#160;  <span class="keyword">static</span> <span class="keywordtype">int</span> NumSqueezableInnerDims(<span class="keyword">const</span> DimensionsMap&amp; dim_map) {</div>
<div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>&#160;    <span class="keywordtype">int</span> num_squeezable_dims = 0;</div>
<div class="line"><a name="l01353"></a><span class="lineno"> 1353</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; NumDims; ++i) {</div>
<div class="line"><a name="l01354"></a><span class="lineno"> 1354</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">int</span> dim = IsColMajor ? i : NumDims - i - 1;</div>
<div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>&#160;      <span class="keywordflow">if</span> (dim_map[dim] != dim) <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>&#160;      num_squeezable_dims++;</div>
<div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>&#160;    }</div>
<div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>&#160;    <span class="keywordflow">return</span> num_squeezable_dims;</div>
<div class="line"><a name="l01359"></a><span class="lineno"> 1359</span>&#160;  }</div>
<div class="line"><a name="l01360"></a><span class="lineno"> 1360</span>&#160;};</div>
<div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>&#160; </div>
<div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>&#160;<span class="comment">// -------------------------------------------------------------------------- //</span></div>
<div class="line"><a name="l01363"></a><span class="lineno"> 1363</span>&#160;<span class="comment">// TensorBlockAssignment assigns a block expression of type `TensorBlockExpr` to</span></div>
<div class="line"><a name="l01364"></a><span class="lineno"> 1364</span>&#160;<span class="comment">// a Tensor block defined by `desc`, backed by a memory buffer at `target`.</span></div>
<div class="line"><a name="l01365"></a><span class="lineno"> 1365</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>&#160;<span class="comment">// Currently there is no way to write from a Tensor expression to a block of</span></div>
<div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>&#160;<span class="comment">// memory, if dimensions are reordered. If you need to do that, you should</span></div>
<div class="line"><a name="l01368"></a><span class="lineno"> 1368</span>&#160;<span class="comment">// materialize a Tensor block expression into a memory buffer, and then use</span></div>
<div class="line"><a name="l01369"></a><span class="lineno"> 1369</span>&#160;<span class="comment">// TensorBlockIO to copy data between two memory buffers with a custom</span></div>
<div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>&#160;<span class="comment">// `target-&gt;src` dimension map (see definition above).</span></div>
<div class="line"><a name="l01371"></a><span class="lineno"> 1371</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l01372"></a><span class="lineno"> 1372</span>&#160;<span class="comment">// Also currently the innermost dimension of `target` must have a stride &#39;1&#39;</span></div>
<div class="line"><a name="l01373"></a><span class="lineno"> 1373</span>&#160;<span class="comment">// (contiguous in memory). This restriction could be lifted with a `pscatter`,</span></div>
<div class="line"><a name="l01374"></a><span class="lineno"> 1374</span>&#160;<span class="comment">// but in practice it&#39;s never needed, and there is a similar TensorBlockIO</span></div>
<div class="line"><a name="l01375"></a><span class="lineno"> 1375</span>&#160;<span class="comment">// workaround for that.</span></div>
<div class="line"><a name="l01376"></a><span class="lineno"> 1376</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l01377"></a><span class="lineno"> 1377</span>&#160;<span class="comment">// TODO(ezhulenev): TensorBlockAssignment is a special case of TensorBlockIO</span></div>
<div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>&#160;<span class="comment">// where `src` is a tensor expression. Explore if it is possible to rewrite IO</span></div>
<div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>&#160;<span class="comment">// to use expressions instead of pointers, and after that TensorBlockAssignment</span></div>
<div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>&#160;<span class="comment">// will become an alias to IO.</span></div>
<div class="line"><a name="l01381"></a><span class="lineno"> 1381</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Scalar, <span class="keywordtype">int</span> NumDims, <span class="keyword">typename</span> TensorBlockExpr,</div>
<div class="line"><a name="l01382"></a><span class="lineno"> 1382</span>&#160;          <span class="keyword">typename</span> IndexType = <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Eigen::Index</a>&gt;</div>
<div class="line"><a name="l01383"></a><span class="lineno"> 1383</span>&#160;<span class="keyword">class </span>TensorBlockAssignment {</div>
<div class="line"><a name="l01384"></a><span class="lineno"> 1384</span>&#160;  <span class="comment">// We will use coeff/packet path to evaluate block expressions.</span></div>
<div class="line"><a name="l01385"></a><span class="lineno"> 1385</span>&#160;  <span class="keyword">typedef</span> TensorEvaluator&lt;const TensorBlockExpr, DefaultDevice&gt;</div>
<div class="line"><a name="l01386"></a><span class="lineno"> 1386</span>&#160;      TensorBlockEvaluator;</div>
<div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>&#160; </div>
<div class="line"><a name="l01388"></a><span class="lineno"> 1388</span>&#160;  <span class="keyword">typedef</span> DSizes&lt;IndexType, NumDims&gt; Dimensions;</div>
<div class="line"><a name="l01389"></a><span class="lineno"> 1389</span>&#160; </div>
<div class="line"><a name="l01390"></a><span class="lineno"> 1390</span>&#160;  <span class="keyword">enum</span> {</div>
<div class="line"><a name="l01391"></a><span class="lineno"> 1391</span>&#160;    Vectorizable = packet_traits&lt;Scalar&gt;::Vectorizable,</div>
<div class="line"><a name="l01392"></a><span class="lineno"> 1392</span>&#160;    PacketSize = packet_traits&lt;Scalar&gt;::size</div>
<div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>&#160;  };</div>
<div class="line"><a name="l01394"></a><span class="lineno"> 1394</span>&#160; </div>
<div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>&#160;  <span class="keyword">template</span> &lt;<span class="keywordtype">bool</span> Vectorizable, <span class="keyword">typename</span> Evaluator&gt;</div>
<div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>&#160;  <span class="keyword">struct </span>InnerDimAssign {</div>
<div class="line"><a name="l01397"></a><span class="lineno"> 1397</span>&#160;    EIGEN_ALWAYS_INLINE <span class="keyword">static</span> <span class="keywordtype">void</span> Run(Scalar* target, IndexType count,</div>
<div class="line"><a name="l01398"></a><span class="lineno"> 1398</span>&#160;                                        <span class="keyword">const</span> Evaluator&amp; eval,</div>
<div class="line"><a name="l01399"></a><span class="lineno"> 1399</span>&#160;                                        IndexType eval_offset) {</div>
<div class="line"><a name="l01400"></a><span class="lineno"> 1400</span>&#160;      <span class="keywordflow">for</span> (IndexType i = 0; i &lt; count; ++i) {</div>
<div class="line"><a name="l01401"></a><span class="lineno"> 1401</span>&#160;        target[i] = eval.coeff(eval_offset + i);</div>
<div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>&#160;      }</div>
<div class="line"><a name="l01403"></a><span class="lineno"> 1403</span>&#160;    }</div>
<div class="line"><a name="l01404"></a><span class="lineno"> 1404</span>&#160;  };</div>
<div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>&#160; </div>
<div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Evaluator&gt;</div>
<div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>&#160;  <span class="keyword">struct </span>InnerDimAssign&lt;true, Evaluator&gt; {</div>
<div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>&#160;    EIGEN_ALWAYS_INLINE <span class="keyword">static</span> <span class="keywordtype">void</span> Run(Scalar* target, IndexType count,</div>
<div class="line"><a name="l01409"></a><span class="lineno"> 1409</span>&#160;                                        <span class="keyword">const</span> Evaluator&amp; eval,</div>
<div class="line"><a name="l01410"></a><span class="lineno"> 1410</span>&#160;                                        IndexType eval_offset) {</div>
<div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>&#160;      <span class="keyword">typedef</span> <span class="keyword">typename</span> packet_traits&lt;Scalar&gt;::type Packet;</div>
<div class="line"><a name="l01412"></a><span class="lineno"> 1412</span>&#160; </div>
<div class="line"><a name="l01413"></a><span class="lineno"> 1413</span>&#160;      <span class="keyword">const</span> IndexType unrolled_size = count - 4 * PacketSize;</div>
<div class="line"><a name="l01414"></a><span class="lineno"> 1414</span>&#160;      <span class="keyword">const</span> IndexType vectorized_size = count - PacketSize;</div>
<div class="line"><a name="l01415"></a><span class="lineno"> 1415</span>&#160;      IndexType i = 0;</div>
<div class="line"><a name="l01416"></a><span class="lineno"> 1416</span>&#160; </div>
<div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>&#160;      <span class="keywordflow">for</span> (; i &lt;= unrolled_size; i += 4 * PacketSize) {</div>
<div class="line"><a name="l01418"></a><span class="lineno"> 1418</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; 4; ++j) {</div>
<div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>&#160;          <span class="keyword">const</span> IndexType idx = eval_offset + i + j * PacketSize;</div>
<div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>&#160;          Packet p = eval.template packet&lt;Unaligned&gt;(idx);</div>
<div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>&#160;          pstoreu&lt;Scalar&gt;(target + i + j * PacketSize, p);</div>
<div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>&#160;        }</div>
<div class="line"><a name="l01423"></a><span class="lineno"> 1423</span>&#160;      }</div>
<div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>&#160; </div>
<div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>&#160;      <span class="keywordflow">for</span> (; i &lt;= vectorized_size; i += PacketSize) {</div>
<div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>&#160;        Packet p = eval.template packet&lt;Unaligned&gt;(eval_offset + i);</div>
<div class="line"><a name="l01427"></a><span class="lineno"> 1427</span>&#160;        pstoreu&lt;Scalar&gt;(target + i, p);</div>
<div class="line"><a name="l01428"></a><span class="lineno"> 1428</span>&#160;      }</div>
<div class="line"><a name="l01429"></a><span class="lineno"> 1429</span>&#160; </div>
<div class="line"><a name="l01430"></a><span class="lineno"> 1430</span>&#160;      <span class="keywordflow">for</span> (; i &lt; count; ++i) {</div>
<div class="line"><a name="l01431"></a><span class="lineno"> 1431</span>&#160;        target[i] = eval.coeff(eval_offset + i);</div>
<div class="line"><a name="l01432"></a><span class="lineno"> 1432</span>&#160;      }</div>
<div class="line"><a name="l01433"></a><span class="lineno"> 1433</span>&#160;    }</div>
<div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>&#160;  };</div>
<div class="line"><a name="l01435"></a><span class="lineno"> 1435</span>&#160; </div>
<div class="line"><a name="l01436"></a><span class="lineno"> 1436</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l01437"></a><span class="lineno"> 1437</span>&#160;  <span class="keyword">struct </span>Target {</div>
<div class="line"><a name="l01438"></a><span class="lineno"> 1438</span>&#160;    Target(<span class="keyword">const</span> Dimensions&amp; target_dims, <span class="keyword">const</span> Dimensions&amp; target_strides,</div>
<div class="line"><a name="l01439"></a><span class="lineno"> 1439</span>&#160;           Scalar* target_data, IndexType target_offset = 0)</div>
<div class="line"><a name="l01440"></a><span class="lineno"> 1440</span>&#160;        : dims(target_dims),</div>
<div class="line"><a name="l01441"></a><span class="lineno"> 1441</span>&#160;          strides(target_strides),</div>
<div class="line"><a name="l01442"></a><span class="lineno"> 1442</span>&#160;          data(target_data),</div>
<div class="line"><a name="l01443"></a><span class="lineno"> 1443</span>&#160;          offset(target_offset) {}</div>
<div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>&#160; </div>
<div class="line"><a name="l01445"></a><span class="lineno"> 1445</span>&#160;    Dimensions dims;</div>
<div class="line"><a name="l01446"></a><span class="lineno"> 1446</span>&#160;    Dimensions strides;</div>
<div class="line"><a name="l01447"></a><span class="lineno"> 1447</span>&#160;    Scalar* data;</div>
<div class="line"><a name="l01448"></a><span class="lineno"> 1448</span>&#160;    IndexType offset;</div>
<div class="line"><a name="l01449"></a><span class="lineno"> 1449</span>&#160;  };</div>
<div class="line"><a name="l01450"></a><span class="lineno"> 1450</span>&#160; </div>
<div class="line"><a name="l01451"></a><span class="lineno"> 1451</span>&#160;  <span class="keyword">static</span> Target target(<span class="keyword">const</span> Dimensions&amp; target_dims,</div>
<div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>&#160;                       <span class="keyword">const</span> Dimensions&amp; target_strides, Scalar* target_data,</div>
<div class="line"><a name="l01453"></a><span class="lineno"> 1453</span>&#160;                       IndexType target_offset = 0) {</div>
<div class="line"><a name="l01454"></a><span class="lineno"> 1454</span>&#160;    <span class="keywordflow">return</span> Target(target_dims, target_strides, target_data, target_offset);</div>
<div class="line"><a name="l01455"></a><span class="lineno"> 1455</span>&#160;  }</div>
<div class="line"><a name="l01456"></a><span class="lineno"> 1456</span>&#160; </div>
<div class="line"><a name="l01457"></a><span class="lineno"> 1457</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> TargetDimsIndexType, <span class="keyword">typename</span> TargetStr<span class="keywordtype">id</span>esIndexType&gt;</div>
<div class="line"><a name="l01458"></a><span class="lineno"> 1458</span>&#160;  <span class="keyword">static</span> Target target(</div>
<div class="line"><a name="l01459"></a><span class="lineno"> 1459</span>&#160;      <span class="keyword">const</span> DSizes&lt;TargetDimsIndexType, NumDims&gt;&amp; target_dims,</div>
<div class="line"><a name="l01460"></a><span class="lineno"> 1460</span>&#160;      <span class="keyword">const</span> DSizes&lt;TargetStridesIndexType, NumDims&gt;&amp; target_strides,</div>
<div class="line"><a name="l01461"></a><span class="lineno"> 1461</span>&#160;      Scalar* target_data, IndexType target_offset = 0) {</div>
<div class="line"><a name="l01462"></a><span class="lineno"> 1462</span>&#160;    <span class="comment">// DSizes constructor will do index type promotion if it&#39;s safe.</span></div>
<div class="line"><a name="l01463"></a><span class="lineno"> 1463</span>&#160;    <span class="keywordflow">return</span> Target(Dimensions(target_dims), Dimensions(target_strides),</div>
<div class="line"><a name="l01464"></a><span class="lineno"> 1464</span>&#160;                  target_data, target_offset);</div>
<div class="line"><a name="l01465"></a><span class="lineno"> 1465</span>&#160;  }</div>
<div class="line"><a name="l01466"></a><span class="lineno"> 1466</span>&#160; </div>
<div class="line"><a name="l01467"></a><span class="lineno"> 1467</span>&#160;  <span class="keyword">static</span> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE <span class="keywordtype">void</span> Run(</div>
<div class="line"><a name="l01468"></a><span class="lineno"> 1468</span>&#160;      <span class="keyword">const</span> Target&amp; target, <span class="keyword">const</span> TensorBlockExpr&amp; expr) {</div>
<div class="line"><a name="l01469"></a><span class="lineno"> 1469</span>&#160;    <span class="comment">// Prepare evaluator for block expression.</span></div>
<div class="line"><a name="l01470"></a><span class="lineno"> 1470</span>&#160;    DefaultDevice default_device;</div>
<div class="line"><a name="l01471"></a><span class="lineno"> 1471</span>&#160;    TensorBlockEvaluator eval(expr, default_device);</div>
<div class="line"><a name="l01472"></a><span class="lineno"> 1472</span>&#160; </div>
<div class="line"><a name="l01473"></a><span class="lineno"> 1473</span>&#160;    <span class="comment">// Tensor block expression dimension should match destination dimensions.</span></div>
<div class="line"><a name="l01474"></a><span class="lineno"> 1474</span>&#160;    eigen_assert(dimensions_match(target.dims, eval.dimensions()));</div>
<div class="line"><a name="l01475"></a><span class="lineno"> 1475</span>&#160; </div>
<div class="line"><a name="l01476"></a><span class="lineno"> 1476</span>&#160;    <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">int</span> Layout = TensorBlockEvaluator::Layout;</div>
<div class="line"><a name="l01477"></a><span class="lineno"> 1477</span>&#160;    <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> is_col_major = Layout == <a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a0103672ae41005ab03b4176c765afd62">ColMajor</a>;</div>
<div class="line"><a name="l01478"></a><span class="lineno"> 1478</span>&#160; </div>
<div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>&#160;    <span class="comment">// Initialize output inner dimension size based on a layout.</span></div>
<div class="line"><a name="l01480"></a><span class="lineno"> 1480</span>&#160;    <span class="keyword">const</span> IndexType output_size = NumDims == 0 ? 1 : target.dims.TotalSize();</div>
<div class="line"><a name="l01481"></a><span class="lineno"> 1481</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> inner_dim_idx = is_col_major ? 0 : NumDims - 1;</div>
<div class="line"><a name="l01482"></a><span class="lineno"> 1482</span>&#160;    IndexType output_inner_dim_size = target.dims[inner_dim_idx];</div>
<div class="line"><a name="l01483"></a><span class="lineno"> 1483</span>&#160; </div>
<div class="line"><a name="l01484"></a><span class="lineno"> 1484</span>&#160;    <span class="comment">// Target inner dimension stride must be &#39;1&#39;.</span></div>
<div class="line"><a name="l01485"></a><span class="lineno"> 1485</span>&#160;    eigen_assert(target.strides[inner_dim_idx] == 1);</div>
<div class="line"><a name="l01486"></a><span class="lineno"> 1486</span>&#160; </div>
<div class="line"><a name="l01487"></a><span class="lineno"> 1487</span>&#160;    <span class="comment">// Squeeze multiple inner dims into one if they are contiguous in `target`.</span></div>
<div class="line"><a name="l01488"></a><span class="lineno"> 1488</span>&#160;    IndexType num_squeezed_dims = 0;</div>
<div class="line"><a name="l01489"></a><span class="lineno"> 1489</span>&#160;    <span class="keywordflow">for</span> (<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> i = 1; i &lt; NumDims; ++i) {</div>
<div class="line"><a name="l01490"></a><span class="lineno"> 1490</span>&#160;      <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> dim = is_col_major ? i : NumDims - i - 1;</div>
<div class="line"><a name="l01491"></a><span class="lineno"> 1491</span>&#160;      <span class="keyword">const</span> IndexType target_stride = target.strides[dim];</div>
<div class="line"><a name="l01492"></a><span class="lineno"> 1492</span>&#160; </div>
<div class="line"><a name="l01493"></a><span class="lineno"> 1493</span>&#160;      <span class="keywordflow">if</span> (output_inner_dim_size == target_stride) {</div>
<div class="line"><a name="l01494"></a><span class="lineno"> 1494</span>&#160;        output_inner_dim_size *= target.dims[dim];</div>
<div class="line"><a name="l01495"></a><span class="lineno"> 1495</span>&#160;        num_squeezed_dims++;</div>
<div class="line"><a name="l01496"></a><span class="lineno"> 1496</span>&#160;      } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l01497"></a><span class="lineno"> 1497</span>&#160;        <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l01498"></a><span class="lineno"> 1498</span>&#160;      }</div>
<div class="line"><a name="l01499"></a><span class="lineno"> 1499</span>&#160;    }</div>
<div class="line"><a name="l01500"></a><span class="lineno"> 1500</span>&#160; </div>
<div class="line"><a name="l01501"></a><span class="lineno"> 1501</span>&#160;    <span class="comment">// Initialize output block iterator state. Dimension in this array are</span></div>
<div class="line"><a name="l01502"></a><span class="lineno"> 1502</span>&#160;    <span class="comment">// always in inner_most -&gt; outer_most order (col major layout).</span></div>
<div class="line"><a name="l01503"></a><span class="lineno"> 1503</span>&#160;    array&lt;BlockIteratorState, NumDims&gt; it;</div>
<div class="line"><a name="l01504"></a><span class="lineno"> 1504</span>&#160; </div>
<div class="line"><a name="l01505"></a><span class="lineno"> 1505</span>&#160;    <span class="keywordtype">int</span> idx = 0;  <span class="comment">// currently initialized iterator state index</span></div>
<div class="line"><a name="l01506"></a><span class="lineno"> 1506</span>&#160;    <span class="keywordflow">for</span> (<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> i = num_squeezed_dims; i &lt; NumDims - 1; ++i) {</div>
<div class="line"><a name="l01507"></a><span class="lineno"> 1507</span>&#160;      <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> dim = is_col_major ? i + 1 : NumDims - i - 2;</div>
<div class="line"><a name="l01508"></a><span class="lineno"> 1508</span>&#160; </div>
<div class="line"><a name="l01509"></a><span class="lineno"> 1509</span>&#160;      it[idx].count = 0;</div>
<div class="line"><a name="l01510"></a><span class="lineno"> 1510</span>&#160;      it[idx].size = target.dims[dim];</div>
<div class="line"><a name="l01511"></a><span class="lineno"> 1511</span>&#160;      it[idx].output_stride = target.strides[dim];</div>
<div class="line"><a name="l01512"></a><span class="lineno"> 1512</span>&#160;      it[idx].output_span = it[idx].output_stride * (it[idx].size - 1);</div>
<div class="line"><a name="l01513"></a><span class="lineno"> 1513</span>&#160;      idx++;</div>
<div class="line"><a name="l01514"></a><span class="lineno"> 1514</span>&#160;    }</div>
<div class="line"><a name="l01515"></a><span class="lineno"> 1515</span>&#160; </div>
<div class="line"><a name="l01516"></a><span class="lineno"> 1516</span>&#160;    <span class="comment">// We read block expression from the beginning, and start writing data to</span></div>
<div class="line"><a name="l01517"></a><span class="lineno"> 1517</span>&#160;    <span class="comment">// `target` at given offset.</span></div>
<div class="line"><a name="l01518"></a><span class="lineno"> 1518</span>&#160;    IndexType input_offset = 0;</div>
<div class="line"><a name="l01519"></a><span class="lineno"> 1519</span>&#160;    IndexType output_offset = target.offset;</div>
<div class="line"><a name="l01520"></a><span class="lineno"> 1520</span>&#160; </div>
<div class="line"><a name="l01521"></a><span class="lineno"> 1521</span>&#160;    <span class="comment">// Iterate copying data from `eval` to `target`.</span></div>
<div class="line"><a name="l01522"></a><span class="lineno"> 1522</span>&#160;    <span class="keywordflow">for</span> (IndexType i = 0; i &lt; output_size; i += output_inner_dim_size) {</div>
<div class="line"><a name="l01523"></a><span class="lineno"> 1523</span>&#160;      <span class="comment">// Assign to `target` at current offset.</span></div>
<div class="line"><a name="l01524"></a><span class="lineno"> 1524</span>&#160;      InnerDimAssign&lt;Vectorizable &amp;&amp; TensorBlockEvaluator::PacketAccess,</div>
<div class="line"><a name="l01525"></a><span class="lineno"> 1525</span>&#160;                     TensorBlockEvaluator&gt;::Run(target.data + output_offset,</div>
<div class="line"><a name="l01526"></a><span class="lineno"> 1526</span>&#160;                                                output_inner_dim_size, eval,</div>
<div class="line"><a name="l01527"></a><span class="lineno"> 1527</span>&#160;                                                input_offset);</div>
<div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>&#160; </div>
<div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>&#160;      <span class="comment">// Move input offset forward by the number of assigned coefficients.</span></div>
<div class="line"><a name="l01530"></a><span class="lineno"> 1530</span>&#160;      input_offset += output_inner_dim_size;</div>
<div class="line"><a name="l01531"></a><span class="lineno"> 1531</span>&#160; </div>
<div class="line"><a name="l01532"></a><span class="lineno"> 1532</span>&#160;      <span class="comment">// Update index.</span></div>
<div class="line"><a name="l01533"></a><span class="lineno"> 1533</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; idx; ++j) {</div>
<div class="line"><a name="l01534"></a><span class="lineno"> 1534</span>&#160;        <span class="keywordflow">if</span> (++it[j].count &lt; it[j].size) {</div>
<div class="line"><a name="l01535"></a><span class="lineno"> 1535</span>&#160;          output_offset += it[j].output_stride;</div>
<div class="line"><a name="l01536"></a><span class="lineno"> 1536</span>&#160;          <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l01537"></a><span class="lineno"> 1537</span>&#160;        }</div>
<div class="line"><a name="l01538"></a><span class="lineno"> 1538</span>&#160;        it[j].count = 0;</div>
<div class="line"><a name="l01539"></a><span class="lineno"> 1539</span>&#160;        output_offset -= it[j].output_span;</div>
<div class="line"><a name="l01540"></a><span class="lineno"> 1540</span>&#160;      }</div>
<div class="line"><a name="l01541"></a><span class="lineno"> 1541</span>&#160;    }</div>
<div class="line"><a name="l01542"></a><span class="lineno"> 1542</span>&#160;  }</div>
<div class="line"><a name="l01543"></a><span class="lineno"> 1543</span>&#160; </div>
<div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>&#160; <span class="keyword">private</span>:</div>
<div class="line"><a name="l01545"></a><span class="lineno"> 1545</span>&#160;  <span class="keyword">struct </span>BlockIteratorState {</div>
<div class="line"><a name="l01546"></a><span class="lineno"> 1546</span>&#160;    BlockIteratorState()</div>
<div class="line"><a name="l01547"></a><span class="lineno"> 1547</span>&#160;        : count(0), size(0), output_stride(0), output_span(0) {}</div>
<div class="line"><a name="l01548"></a><span class="lineno"> 1548</span>&#160; </div>
<div class="line"><a name="l01549"></a><span class="lineno"> 1549</span>&#160;    IndexType count;</div>
<div class="line"><a name="l01550"></a><span class="lineno"> 1550</span>&#160;    IndexType size;</div>
<div class="line"><a name="l01551"></a><span class="lineno"> 1551</span>&#160;    IndexType output_stride;</div>
<div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>&#160;    IndexType output_span;</div>
<div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>&#160;  };</div>
<div class="line"><a name="l01554"></a><span class="lineno"> 1554</span>&#160;};</div>
<div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>&#160; </div>
<div class="line"><a name="l01556"></a><span class="lineno"> 1556</span>&#160;<span class="comment">// -------------------------------------------------------------------------- //</span></div>
<div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>&#160; </div>
<div class="line"><a name="l01558"></a><span class="lineno"> 1558</span>&#160;}  <span class="comment">// namespace internal</span></div>
<div class="line"><a name="l01559"></a><span class="lineno"> 1559</span>&#160;}  <span class="comment">// namespace Eigen</span></div>
<div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>&#160; </div>
<div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>&#160;<span class="preprocessor">#endif  </span><span class="comment">// EIGEN_CXX11_TENSOR_TENSOR_BLOCK_H</span></div>
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